EXTENDED RANGE INTERFEROMETRIC METHODS AND SYSTEMS
An interferometer estimates at least one interferometric parameter of one or more signals emitted from a source. The interferometer includes at least one phase measurement module for measuring phase differences between the source signals received at different signal receiving sensors. At least one coarse estimate of a sought parameter used to represent the at least one interferometric parameter is generated by processing the one or more signals received from the source. At least one fine estimate of the sought parameter is also generated by processing the at least one coarse sought parameter using the plurality of phase measurements received from the at least one phase measurement module. The at least one fine sought parameter represents the at least one interferometric parameter with greater accuracy than the at least one coarse sought parameter and over an extended range of values in which the sought parameter is not unambiguously determinable using only the plurality of phase measurements.
This application claims the benefit of U.S. Provisional Application Ser. No. 61/306,046 filed on Feb. 19, 2010, the entire contents of which are hereby incorporated by reference.
FIELDEmbodiments described herein relate generally to location systems and methods for calculating the distance to an object or a location of an object by estimating one or more time parameters or angles of arrival and, more specifically, to interferometric systems and methods for estimating locations on the basis of multiple ambiguous phase measurements.
INTRODUCTIONLocation systems are used to estimate the location of objects in one-dimensional, two-dimensional or three-dimensional spaces. To provide this functionality, most location systems operate by measuring angles of arrival, or alternatively some time parameters of a signal emitted or reflected by a located object.
Different structures of location systems utilize different methods to estimate object locations. For example, triangulation is a method used to estimate locations based on angles of arrival (AOA). Trilateration is a method used by some location systems to estimate the location of an object by measuring the time of flight (TOF) or time of arrival (TOA) of a signal emitted from that object to several receivers. In a different method, known as multilateration (also known as hyperbolic positioning), the location of an object may be estimated by computing the time difference of arrival (TDOA) of a signal emitted from that object to three or more receivers.
A user of the location system often needs to be able to determine object locations accurately, with high reliability and over wide ranges. The accuracy and reliability with which the object location may be determined in various systems generally depend on how accurate and reliable are the estimates of AOA, TOF, TOA or TDOA. Location systems often work in conditions where noisy signals are received or where the received signals have multipath propagation. Each of these factors may significantly affect the accuracy and reliability of the AOA, TOF, TOA or TDOA estimates. Interferometric estimation of such parameters is often one of the most accurate methods. It can be used for estimating location information with high accuracy, in wide ranges and with generally good quality and reliability.
SUMMARYSome embodiments described herein relate to a combined estimator. In some embodiments, the combined estimator is for use in an interferometric system, which may include one or more direction finding interferometers or one or more interferometric location systems. In some embodiments, the combined estimator comprises a processor. In some embodiments the combined estimators described herein can be implemented in hardware, in software running on microprocessor, ASIC, or in combination of hardware and software. In some such embodiments, the combined estimator estimates a plurality of parameters, which may be referred to as sought parameters, and which can in turn be used to estimate one or more interferometric parameters of a source signal. In some embodiments the combined estimator also estimates noise parameters that may be independent of the one or more interferometric parameters being estimated by the interferometric system.
In some embodiments, the noise parameters are used to determine the quality of associated estimated parameters. In some embodiments, the noise parameters are used to process or filter associated estimated parameters. In some embodiments, if the noise component is above a threshold then the associated estimated parameters are discarded and therefore are not used in the estimation of the one or more interferometric parameters. Alternatively, in some embodiments, if the noise component is above a threshold then the associated estimated parameters are weighted in such a way that reliable estimates take precedence over unreliable estimates. In this way, the estimate of the one or more interferometric parameters may be improved.
Some embodiments described herein relate to an interferometer for determining an interferometric parameter. The interferometer is configured to: determine a plurality of phase measurement values; determine a noise parameter associated with phase measurement values; determine if the noise parameter is above a threshold; if the noise parameter is above the threshold, discard the associated estimated parameters' values; determine the interferometric parameter based on the non-discarded estimated parameters' values.
In some embodiments, the estimated interferometric parameter may be an angle of arrival of a signal. In some embodiments, the estimated interferometric parameter may be a time parameter of a signal that is used in the interferometer to estimate a location of the signal, such as an object that emitted the signal.
In some embodiments, each phase measurement is a phase difference in signals received by one or more signal sensors. In some embodiments, the phase measurement is a phase difference in signals received at two signal sensors. In some embodiments, the phase difference is outputted by a phase detector coupled to receivers that are in turn coupled to the signal sensors.
In some embodiments, a noise parameter is determined, where the noise parameter is indicative of the level of noise. In some embodiments, the noise parameter is a noise component that is independent of the interferometric parameter.
In some embodiments, at least one sought parameter is determined. In some such embodiments, the interferometric parameters are determined from the sought parameters. In some embodiments, the noise parameter associated with sought parameters is determined. In some embodiments, the noise parameters are used to process or filter associated estimated sought parameters. Thus, in some embodiments, If the noise parameter is above a threshold then the associated sought parameters are discarded and are not used in the determination of the interferometric parameters or, alternatively, are adaptively filtered according to the level of the noise parameter.
Some embodiments described herein relate to a method of determining interferometric parameters, the method comprises: determining a plurality of phase measurement values; determining a noise parameter associated with phase measurement values; determining if the noise parameter value is above a threshold; if the noise parameter value is above the threshold, discarding the associated phase measurement values; and determining the interferometric parameters based on the non-discarded phase measurement values.
Some embodiments described herein relate to an interferometer for estimating at least one interferometric parameter of one or more signals received from a source. The interferometer has at least one phase measurement module configured to determine a plurality of phase measurements of the one or more signals received from a source. At least one coarse sought parameter estimator is configured to determine at least one coarse sought parameter representing the at least one interferometric parameter by processing one or more signals received from the source. A fine sought parameter estimator is configured to process the at least one coarse sought parameter, received from the at least one coarse sought parameter estimator, using the plurality of phase measurements received from the at least one phase measurement module to determine at least one fine sought parameter representing the at least one interferometric parameter with greater accuracy than the at least one coarse sought parameter.
In some embodiments, the fine sought parameter estimator comprises a combined estimator configured to determine at least one partial sought parameter, which represents the interferometric parameter over a narrower range of values than the at least one coarse sought parameter. The combined estimator also may determine at least one noise parameter associated with the plurality of phase measurements by processing the plurality of phase measurements. In some embodiments, the fine sought parameter estimator also comprise at least one partial sought parameter extender configured to calculate the at least one fine sought parameter using the partial sought parameter received from the combined estimator and the coarse sought parameter received from the coarse sought parameter estimator.
In some embodiments, the coarse sought parameter estimator generates the coarse estimate of the sought parameter based on a time difference of arrival of the source signal at a pair of signal receiving antennas determined by comparing the magnitude of the received signals against a threshold level. The time difference of arrival is then normalized by an unambiguous time interval in order to determine the coarse estimate of the sought parameter.
In some embodiments, a partial estimate of the sought parameter is also generated to estimate time parameters unambiguously within the unambiguous time interval. The partial estimate of the sought parameter may be generated based on the measured phase differences. Combining the partial and coarse estimates of the sought parameters then yields the fine estimate of the sought parameter with greater accuracy than the coarse estimate and not limited to the same finite range as the partial estimate.
For a better understanding of the embodiments described herein and to show more clearly how they may be carried into effect, reference will now be made, by way of example only, to the accompanying drawings which show at least one example embodiment, and in which:
The accuracy with which a location system can estimate the location or range of the located signal-emitting object may depend, among other factors, on the accuracy of the time parameter estimation used by the location system. Interferometric phase measurements may be used to achieve very accurate time parameter estimation, which in turn would enable very accurate estimates of the object location. However, interferometric phase measurements of an oscillating signal are often inherently ambiguous, requiring multiple estimations of the same time parameter to resolve the inherent ambiguity. The requirement of multiple time parameter estimations tends to increase overall system complexity, for example, in terms of additional hardware components or additional computing resources.
The embodiments described herein generally relate to interferometric systems and methods that are operable to resolve the inherent ambiguity in time parameter estimation without incurring undue system complexity. Certain of the described embodiments may be applied to radars of different types and configurations, as well as other forms of location and/or navigation systems. Some of the embodiments process ambiguous phase measurements in order to produce estimates of one or more time parameters, as described above, such as time of flight (TOF), time of arrival (TOA) and time difference of arrival (TDOA). Some of the embodiments are also operable to process ambiguous phase measurements in order to estimate of one or more Angles of Arrival (AOA), as is the case of direction finding interferometers. For convenience, reference may be made primarily to interferometric systems and related methods for locating objects by measuring time difference of arrival of one or more signals emitted by the object. Considered interferometric methods are generalized on estimation of several interferometric parameters, which can be used in direction finding interferometers estimating more than one angle of arrival.
Various interferometric systems are known in the art. Many of these interferometric systems are direction finding interferometers that utilize an antenna array in order to estimate the AOA of an incoming signal, which characterizes the direction from the antenna array to the located object. Depending on the application, the antenna array may be a linear antenna array capable of measuring one angle of arrival, a planar antenna array capable of measuring two angles of arrival, or a three-dimensional antenna array capable of measuring more than two different angles of arrival.
Phase interferometers for use in location systems may be implemented using an array of several spatially separated receiving antennas, where the respective location of each antenna in the array is known. In such systems, the time parameter measured is often TDOA, although other time parameters, such as those referenced above, can also be measured instead. In some embodiments, a located object emits pulsed signals with known carrier frequencies that are detected in turn by the receiving antennas. The utilized time parameter (e.g. TDOA) may be estimated as the elapsed time between the beginnings of respective signal pulses received at different antenna pairs in the system.
If the signals emitted by the located object have wide spectral bandwidth, the pulses received at each antenna have a relatively sharp rise to full signal amplitude. The start of each pulse may then be relatively easy to detect with good accuracy. However, not all signals emitted from the located object will have wide spectral bandwidth. In particular, if the signals emitted by the located object do not have a wide spectral bandwidth, the pulses received at each antenna may have a relatively slow rise to full signal amplitude, which can make it difficult to accurately detect the start of each signal pulse. In these cases, accurate estimates of TDOA may be difficult to produce based on pulse arrival times. Therefore, estimating the start of signals received at different antennas may be used in some cases as a course estimate of TDOA. Pulse arrival times may also be used to produce initial or intermediate estimates of TDOA.
To provide a finer estimate of this time parameter, in addition to measuring the start times of the signal pulses received at different antenna, the phase difference between like frequency signals received at different antennas may also be measured. In general, the shorter the wavelength of the signal used for measuring phase difference, the more accurate will be the estimate of a given time parameter, such as TDOA. Reducing the wavelength of the received signals therefore provides one way to improve the accuracy of the time parameter estimate.
However, when measuring the phase difference between two signals received at a pair of antennas, an inherent ambiguity will generally arise if the distance between the pair of receiving antennas is greater than one half wavelength of the received signals. In that case, the actual phase difference between the two received signals can be much more than 360° and, yet, not be fully detected because phase difference is only measurable within a 360° range. Consequently, integer numbers of whole cycles of phase differences can be missed in the measurements of phase. The integer numbers of whole cycles are often reproduced through subsequent processing of the phase measurements in order to provide unambiguous time parameter estimation.
The described embodiments are operable with located objects that emit pulsed signals having multiple different known frequency components (or alternatively multiple different known wavelengths). By measuring multiple different phase differences between like frequency components of the emitted signals received at different receiving antennas, the described embodiments provide for ambiguity resolution and fine estimation of time parameters. The fine estimate of the time parameters may be provided instead of, or in addition to, the course estimate of the time parameter generated using pulse start times, as will be explained in more detail below.
Another difficulty with phase measurements in interferometric location systems is that the multipath of signal propagation can introduce significant phase measurement errors. This effect, together with errors associated with other noise components of the received signals, can significantly decrease the probability of correct ambiguity resolution. For example, ambiguity resolution in interferometric systems can be incorrect if the sum of all phase errors in the phase measurements is above a given threshold level. This limit can vary depending on the particular configuration of the interferometer and is selectable in various embodiments.
If the sum of all phase errors in the phase measurements is above the given threshold level, then the probability of incorrect ambiguity resolution for the corresponding phase measurements is high. This in turn can mean that the result of the phase measurements is unreliable. Accordingly, in various embodiments, phase measurements having a corresponding amount of phase noise that is above the threshold level of noise can be rejected or specifically processed to improve the accuracy of the time parameter estimation.
Moreover, in various embodiments, the level of noise in the phase measurements is used to characterize the quality of the time parameter estimate. In some of the described embodiments, both noise parameters and the estimate of the time parameter are computed concurrently. In some embodiments, the noise parameters are analyzed in order to estimate the degree of phase errors present in the phase measurements and, upon that basis, determine the reliability of the resulting time parameter estimate. In some such embodiments, if a particular estimate or sample of a time parameter is determined to be unreliable, then that particular estimate is discarded and not used in an overall estimate of the time parameter. Discarding unreliable estimates of the time parameter can improve the overall accuracy of the interferometric location system.
Some embodiments described herein relate to an interferometric location system that produces a fine sought parameter estimate ΘF, that is obtained as a combination of a coarse sought parameter estimate ΘC and a partial sought parameter estimate ΘP. The partial sought parameter estimate ΘP has generally greater accuracy compared to the coarse sought parameter estimate ΘC, but is also defined within a more limited range of values. In some cases, the coarse sought parameter estimate ΘC may exist within limits that significantly exceed the limits imposed on the partial sought parameter estimate ΘP. The fine sought parameter estimate ΘF may combine both the accuracy of the partial sought parameter estimate ΘP and the extended range of the coarse sought parameter estimate ΘC.
Various embodiments described herein relate to an interferometric location system that estimates a partial sought parameter ΘP, which is related to an interferometric parameter, such as a time parameter estimated by the interferometric location system to determine the position or range of a located object. Each of the partial sought parameter ΘP and the fine sought parameter estimate ΘF is related to one or more noise parameters determined after processing N phase differences φ1, φ2, . . . , φN measured on N signal components received at pairs of spatially separated antennas and having different wavelengths with respect to one another. In some embodiments, the time parameter to which the various sought parameters ΘF, ΘP, and ΘC are related can represent any of TOA, TOF, or TDOA.
While reference may be made primarily to interferometric location systems that estimate a time parameter, the described embodiments may also be suitable for use in direction finding interferometers. Accordingly, in some embodiments, the sought parameter estimates ΘF, ΘP, and ΘC may relate to estimates of angle of arrival.
In some embodiments, the noise parameters computed and used by the interferometric location system are independent of the particular interferometric parameter, e.g. TOF, TOA, TDOA and AOA, which the interferometric location system is configured to estimate. For example, for some interferometric location systems that are made in accordance with the embodiments disclosed herein, the noise parameters are independent of the position of the located object. In some embodiments, the noise parameters characterize the multipath components of the received signals.
Referring now to
For example, while
The signal receiving sensors 1110 are spatially distributed within a plane in which the object 1105 is located. The respective locations of the signal receiving sensors 1110 are not necessarily fixed, but are generally known at the moment the signal emitted by the located object 1105 is received at each respective signal receiving sensor 1110. In some embodiments, the signal receiving sensors 1110 are fixed (i.e. stationary) and their locations are known. In some embodiments, the signal receiving sensors 1110 are mobile (i.e. transitory), but have tracked or otherwise knowable trajectories from which their respective locations can be continuously determined.
The located object 1105 emits multiple frequency component signals from which at least N frequency components related to each other as relatively prime numbers may be selected. In some embodiments, the signals emitted by the located object 1105 have at least N components with different known frequencies that relate to each other as relatively prime numbers. The known frequencies may not themselves be prime or relatively prime numbers, but instead relate to each other as relatively prime numbers after being divided by a common multiplier. For example, the signal emitted by the located object 1105 can have two component frequencies f1=6 MHz and f2=10 MHz. After dividing through by a common multiplier of 2×106 Hz, the ratio of these two component frequencies is 3 to 5, which are relatively prime numbers. As used throughout the description, the phrase “relating to one another as relatively prime numbers” may have this general meaning.
In some embodiments, the located object 1105 emits signals that have at least N+1 components with known frequencies that can be combined to produce at least N signal components with different known frequencies that relate to each other as N relatively prime numbers. For example, the signal emitted by the located object 1105 can have three component frequencies f1=1000 MHz, f2=1030 MHz and f3=1040 MHz. These three signal components may be combined to yield two components with frequencies f4=f2−f1=30 MHz and f5=f3−f1=40 MHz. After dividing through by a common multiplier of 107 Hz, the combined component frequencies f4 and f5 are in the ratio 3 to 4, which are relatively prime numbers. Additional aspects of the frequency components, and how they may relate to each other through the common multiplier as relatively prime numbers, will be discussed further below.
In some cases, the located object 1105 concurrently emits multiple signal components from which the at least N signal components related to each other as relatively prime numbers may be directly selected or otherwise obtained by combining signal components. In other cases, the located object 1105 emits the multiple signal components sequentially, for example, in accordance with a frequency hopping protocol.
Depending on the location of the object 1105 and the relative positioning of the signal receiving sensors 1110, the emitted signal can arrive at the signal receiving sensors 1110 at corresponding different times. Accordingly, the time parameters of the received signals, e.g. TDOA, which the interferometric location system 1100 utilizes to estimate the position of the located object 1105 may generally depend also on the location of the object 1105 and the relative positioning of the signal receiving sensors 1110. In addition, the difference in arrival times of the emitted signal at different signal receiving sensors 1110 also results in the received signals having generally different phases relative to one another.
The signals emitted by the located object 1105 are received at the signal receiving sensors 1110, after which the signals pass through corresponding receivers 1120 and signal transmitting channels 1121 to a plurality of extended interferometers 1130. In some embodiments, one receiver 1120 and one signal transmitting channel 1121 is associated with each signal receiving sensor 1110 to pass the received signals to the plurality of extended interferometers 1130. In various embodiments, the signal transmitting channels 1121 can be cables connected between a respective signal receiver 1120 and extended interferometer 1130, although other types of signal transmitting channels 1121 are possible as well.
Each extended interferometer 1130 may be associated with a corresponding pair of signal receiving antennas 1120, so that the interferometric location system 1100 may include X signal receiving sensors 1110 and X−1 extended interferometers 1130. As illustrated in
In some embodiments, each extended interferometer 1130, which is provided with the signals received at a different pair of the signal receiving sensors 1110, can comprise a phase measurement module 1140, a coarse sought parameter estimator 1150, and a fine sought parameter estimator 1160. The respective pair of signals received at the extended interferometers 1130 are passed to both the phase measurement module 1140 and the coarse sought parameter estimator 1150, so that each of the phase measurement module 1140 and the coarse sought parameter estimator 1150 within a given extended interferometer 1130 receives the same pair of signals for processing.
The phase measurement module 1140 measures N phase differences φi, 1<=i<=N corresponding to N like frequency components of the signals received, or otherwise obtained by combining the signals received, at different pairs of the signal receiving sensors 1110. However, in various embodiments, the phase measurement module 1140 can also pre-process the N phase differences φi including, but not restricted to, averaging, filtering, and decorrelation of phase measurements made on different frequencies. The N phase differences φi generated and output by the phase measurements module 1140 are passed as inputs into the fine sought parameter estimator 1160. A coarse sought parameter estimate ΘC generated and output by the coarse sought parameter estimator 1150 is also passed to the fine sought parameter estimator 1160.
In some embodiments, the phase measurements φi, 1<=i<=N, as well as the estimation of coarse sought parameters ΘC can be organized in different suitable ways, based on the particular configuration or application of the interferometric location system. Thus, in some embodiments, fine sought parameter estimator can be used in various extended interferometers for accurate estimation of AOA, TDOA, TOF, or TOA in different interferometric location systems, where one or more phase measurement modules can be used to determine the phase measurements φi, 1<=i<=N corresponding to the particular system. Likewise, one or more coarse sought parameter estimators can be used to determine the coarse sought parameters corresponding to the particular configuration or application of the interferometric location system.
In various embodiments, the fine sought parameter estimator 1160 processes the N phase differences φi and the coarse sought parameter estimate ΘC to produce the fine sought parameter estimate ΘF. To generate the fine sought parameter estimate ΘF, the fine sought parameter estimator 1160 calculate a partial sought parameter estimate ΘP on the basis of the N phase differences φi without using the coarse sought parameter estimate ΘC. The fine sought parameter estimator 1160 can then generate the fine sought parameter estimate ΘF using the partial sought parameter estimate ΘP combined with the coarse sought parameter estimate ΘC.
As explained in more detail below, the partial sought parameter estimate ΘP corresponds to a time interval tPM inside of which the interferometric parameter can be detected accurately and unambiguously based on the N phase differences φi obtained from the phase measurement module 1140. In some embodiments, the partial sought parameter estimate ΘP is also restricted to values within the range −0.5≦ΘP<0.5. The coarse sought parameter estimate ΘC is not restricted to the same range as the partial sought parameter estimate ΘP and may be any dimensionless real number representing an estimate of the time parameter, e.g. TDOA, normalized by the value of unambiguous time interval tPM. In a particular case in which the time parameter falls within the time interval tPM, the coarse sought parameter estimate ΘC is valued within the range −0.5≦ΘC<0.5 and corresponds to the partial sought parameter estimate ΘP although with less accuracy.
The interferometric location system 1110 also includes a location calculator 1170 coupled to each extended interferometer 1130. Each fine sought parameter estimate ΘF generated by a corresponding fine sought parameter estimator 1160 is passed as an input to the location calculator 1170, which reconstructs the measured time parameters from the fine sought parameter estimates ΘF. The location calculator 1170 then may use any suitable method of calculating coordinates for the located object 1105 using the time parameters. For example, possible methods are described in Y. T. Chan and K. C. Ho, Solution and Performance Analysis of Geolocation by TDOA, IEEE Transactions on Aerospace and Electronic Systems, Vol. 29, No. 4, 1993 and in Wade H. Foy, Position-Location Solutions by Taylor-Series Estimation, IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-12, No. 2, pp. 187-193, 1976. As illustrated in
In alternative embodiments, the location calculator 1170 calculates three spatial coordinates, e.g. x, y and z coordinates, of the located object 1105. However, it should be appreciated that at least one additional signal receiving sensor 1110, receiver 1120, signal transmitting channel 1121, and extended interferometer 1130 may each be included in the location calculator 1170 to calculate the additional spatial coordinate.
Referring now to
Each of the graphs 2100 and 2200 also has defined a threshold value Th, which is at the same level on each graph 2100 and 2200. The time point t1 on graph 2100 represents the first time at which the magnitude S1(t) of signal 2105 exceeds the threshold value Th, while time point t2 on graph 2200 represents the corresponding first time at which the magnitude S2(t) of signal 2205 exceeds the threshold value Th.
In various embodiments, the coarse sought parameter estimator 1150 estimates the time difference of arrival between the two signals 2105 and 2205 at respective signal receiving sensors 1110 as the difference between the first times each of the signals 2105 and 2205 exceeds the threshold value Th, i.e. TDOA=t2−t1. To generate the coarse sought parameter estimate ΘC, the coarse sought parameter estimator 1150 then normalizes the time difference of arrival t2−t1 by the unambiguous time interval tPM defined above. However, the course sought parameter estimator 1150 may also generate the coarse sought parameter estimate ΘC differently in alternative embodiments.
It can be seen from
Referring back to
The range between the phase center of a signal emitting antenna of the located object 1105 and that of a jth signal receiving sensor 1110 can be represented as Rj. Analogously, Rm may be used to represent the range between the phase centers of the signal emitting antenna of the located object 1105 and an mth signal receiving sensor 1110. Then a range difference AR defined for the jth and mth signal receiving sensors 1110 can be calculated according to:
ΔR=Rj−Rm. (1)
The phase difference φi represents a measured phase difference between ith signals components of the same frequency fi received at the jth and mth signal receiving sensors 1110. In various embodiments, phase measurement module 1140 can measure each phase difference φi within the limits −π≦φi<π, which is equivalent to −0.5≦φi<0.5 using normalized phase values. It will be assumed herein through that the measured phase differences φi are normalized.
In some embodiments, the measured phase differences φi relate to the range difference ΔR according to:
where the measured phase differences φi in equation (2) above may be further defined as:
φi=φ0i+ni. (3)
In equations (2) and (3), ni represents a phase error associated with the given phase difference φi, and φ01 represents an ideal phase difference that would be measured between ith signals components if no phase errors were present, i.e., if ni=0 in equation (3).
Because the phase measurement module 1140 measures the phase differences φi only within one cycle, integer multiples ki of full cycles of the phase differences φi may be lost during the phase measurements. In various embodiments, as explained in greater detail below, the integer multiples ki of full cycles are recovered implicitly in generating the partial sought parameter estimate ΘP.
The wavelength λi and frequency fi of the ith signal component are related according to:
where c is a speed of signal propagation of the ith signal component. Moreover, in equation (2), phase shifts due to signal propagation from the signal receiving sensors 1110 to the phase measurement module 1140 are accounted for according to:
φCHi+kCHi=φCHj+kCHj−φCHm−kCHm. (5)
In equation (5) above, φCHi is limited to values in the range −0.5≦φCHi<0.5 and represents a partial phase difference of signal phase shifts that result due to propagation of signals received at the jth and mth signal receiving sensors 1110, respectively, to the phase measurement module 1140. The value of φCHi and integer multiples kCHi of full cycles of φCHi can be estimated and known after calibration of the interferometric location system 1100. Alternatively, these values can be calculated based upon particular locations of the signal receiving sensors 1110 at the moment phase measurements are taken.
To the extent that the values of φCHi and integer multiples kCHi are known or ascertainable, and moreover do not depend on the particular ranges Rj and Rm, the phase measurement module 1140 can compensate for the effects of φCHi and kCHi on the measured phase difference φi. In the discussion following below, it will be assumed that the phase measurement module 1140 compensates for the effects of φCHi and kCHi, with the result that the measured phase difference φi output by the phase measurement module 1140 does not generally depend on these quantities in at least some embodiments.
Applying the above-stated assumptions, equation (1) can be re-written according to:
Taking equation (4) into consideration, equation (6) can then be re-written as:
φi+ki=tP*fi+ni, (7)
where tP is a partial time parameter that is defined as the ratio ΔR/c and represents the time difference of arrival between the signals received at the jth and mth signal receiving sensors 1110, respectively. The component frequencies fi, 1≦i≦N, which relate to each other as relatively prime numbers, may be represented as:
fi=βai, (8)
where β is a common multiplier shared by each fi and where ai, 1≦i≦N, represent the relatively prime numbers through which the component frequencies fi relate.
By processing the N phase differences φi, the partial time parameter tP may be estimated unambiguously within the limits:
where tPM represents a maximum partial time parameter and is defined as:
In equation (10) above, ΔRMAX represents a maximum range difference that may be unambiguously estimated based on N phase differences φi, which measured between signals received at two different signal sensors 1110. The maximum range difference ΔRMAX can be calculated as:
ΔRMAX=aiλi, (11)
for any 1≦i≦N. According to equation (11), each relatively prime number ai represents the number of range spans, expressed in terms of a corresponding wavelength λI, within the maximum range difference ΔRMAX.
Using equations (4) and (8), the wavelength λi can be represented as:
Substituting equation (12) into equation (11) also yields:
Correspondingly, by combining equations (13) and (10), the maximum partial time parameter tPM can be further calculated as:
and thereby also is related inversely to the common multiplier β. According to equation (9), the partial time parameter tP that is defined in equation (7) can be represented as:
tP=tPMΘP, (15)
where ΘP represents the partial sought parameter estimate and, as noted above, is limited to values within the range −0.5≦ΘP<0.5.
Referring now to
Graphs 3200 and 3300 plot the magnitude of different measured phase differences φi as a function of the fine sought parameter estimate ΘF for different frequency components fi. In particular, curve 3205 on graph 3200 plots φ1(ΘF), which represents the relationship between a measured phase difference φ1 and the fine sought parameter estimate ΘF for a first frequency component f1. Likewise curve 3305 on graph 3300 plots φ2(ΘF), which represents the relationship between a measured phase difference φ2 and the fine sought parameter estimate ΘF for a second frequency component f2. It is assumed in curves 3205 and 3305 that n1=n2=0, which reflects the assumption of no noise present in the measured phase differences φi.
After dividing through by the common multiplier β, the frequency components f1 and f2 are in the ratio of 3 to 4, which are relatively prime numbers. This is seen in
Graphs 3200 and 3300 also illustrate the relationships between the partial sought parameter estimate ΘP, the coarse sought parameter estimate ΘC, and the fine sought parameter ΘF. According to some embodiments, the partial sought parameter estimate ΘP is defined within the range −0.5≦ΘP<0.5 inside of which the partial sought parameter estimate ΘP and the fine sought parameter ΘF would be equal. However, if the fine sought parameter estimate ΘF is outside the range of the partial sought parameter ΘP, these two values would not equate.
For three different values of the partial sought parameter estimate ΘP within the range −0.5≦ΘP<0.5 the measured phase difference φ1 is equal to a given value, denoted by 3210 in graph 3200. Point 3220 represents one such value of the partial sought parameter estimate ΘP. Similarly, there are four different values of the partial sought parameter estimate ΘP within the range −0.5≦ΘF<0.5 at which the measured phase difference φ2 is equal to a given value, denoted by 3310 in graph 3300. Point 3320 represents one such value of the partial sought parameter estimate ΘP for which this is true. Points 3220 and 3320 together represent the only pairing in which the partial sought parameter estimate ΘP is the same for each measured phase difference φi. Accordingly, this value of the partial sought parameter estimate ΘP results from the measured phase differences φ1 and φ2 being equal to values 3210 and 3310, respectively.
Moreover, the partial sought parameter estimate ΘP obtained from values 3210 and 3310 of the measured phase differences φ1 and φ2 can be ambiguously represented at multiple different points on the graphs 3200 and 3300. These points correspond to integer multiples of whole numbers added to the partial sought parameter estimate ΘP, which are denoted on the x-axis of graphs 3200 and 3300 as ΘP±1, ΘP±2, ΘP±3, etc. The partial sought parameter estimate ΘP may be ambiguously represented by a multiple of additional values outside of the range −0.5≦ΘP<0.5 to reflect the fact that the TDOA between received signals, i.e. t2−t1, may be greater than the maximum partial time parameter tPM defined in equation (14). By providing a rough estimate of the fine sought parameter estimate ΘF, the course sought parameter ΘC can be used to produce the fine estimate of the sought parameter ΘF from the partial sought parameter estimate ΘP and some integer number of full cycles.
Considering all phase differences φi measured on N frequency components fi, and by combining equations (8), (14), and (15) together, equation (7) can be written in vector form according to:
φ+k=AΘP+n, (16)
where φ, k, A and n are each N-dimensional column vectors, with every ith element in the column vector corresponding to a respective vector component determined for the ith frequency component of the signal received at the signal receiving sensors 1110. In accordance with various embodiments, the value of the partial sought parameter ΘP may be estimated, as will be described, by solving equation (16).
Referring now to
More specifically,
In the alternative embodiments illustrated by in
In some embodiments, the postprocessor 4300 uses the at least one noise parameter received from the combined estimator 4200 to improve the estimate of the fine sought parameter ΘF via processing or filtering fine sought parameter estimates ΘF based on the value of at least one noise parameter. Thus, in some embodiments, the postprocessor 4300 uses the at least one noise parameter received from the combined estimator 4200 to discard any fine sought parameter estimates ΘF that are determined to be unreliable. For example, the fine sought parameter estimates ΘF may be determined to be unreliable if the level of noise associated with the vector φ of measured phase differences exceeds a threshold noise level. As an alternative to discarding, an adaptive filtering of the fine sought parameter estimates ΘF can be performed based on the value of the at least one noise parameter. Thus, in some embodiments, the postprocessor may apply a weighting factor to each fine sought parameter estimate ΘF based on the at least one noise parameter. Fine sought parameter estimates ΘF generated from less noisy phase differences φ may be weighted more heavily than fine sought parameter estimates ΘF generated from more noisy phase differences φ. As a still further alternative, the postprocessor 4200 may combine the fine sought parameter estimate ΘF and the at least one noise parameter into a single vector for processing by some other component of the location system based on the value of the at least one noise parameter.
In various embodiments of the fine sought parameter estimator 1160, of which
ΘF=Θ*C+ΘP. (17)
In equation (17) above, Θ*C represents a corrected coarse sought parameter equal to:
where |X| is an absolute value of X. Moreover, ΘCR and ΔΘC introduced in equation (17) are defined according to:
ΘCR=rnd[ΘC], (19)
and
ΔΘC=rrni{ΘC}, (20)
where rnd[ . . . ] is a procedure of rounding to the nearest integer of an element inside of the square brackets [ . . . ], and where rrni{ . . . } is a procedure of calculating the residual of rounding to the nearest integer of an element inside of the braces { . . . }.
Each fine sought parameter estimator 1160 of a corresponding extended interferometer 1130 (
tF=tPMΘF. (21)
As the fine sought parameter estimate ΘF is not restrained to the range −0.5≦ΘP<0.5 defined for the partial sought parameter ΘP, and may instead be any real number, the unambiguous time parameter tF is not limited to values less than the maximum partial time parameter tPM. Having calculated the unambiguous time parameters tF using equation (21), the location calculator 1170 then determines spatial coordinates of the located object 1105 using any known method.
In various embodiments, either the combined estimator 4100 (
In addition to the interferometric location system 1100 (
The direction finding interferometer may comprise a linear antenna array having baselines between respective antennas, which sizes relate to each other as relatively prime numbers. In such implementations, equation (16) is applicable and may be solved to compute the partial sought parameter estimate ΘP as herein described. However, when applied to estimating AOA in a direction finding interferometer, the various parameters defined in equation (16) may represent different physical quantities as compared to a TDOA interferometric location system. In particular, φ represents a vector of N phase differences measured on the N baselines, the elements of vector k represent numbers of full cycles lost in the phase measurements taken on corresponding baselines, A represents a vector of relatively prime numbers which define corresponding sizes of the N baselines, and the elements of vector n represent phase errors associated with the phase measurements taken on corresponding baselines. The partial sought parameter estimate ΘP solved using equation (16) represents a cosine or sine of the AOA of the source signals. In this way, equation (16) has applicability to both direction finding interferometers for estimating angle of arrival and interferometric location systems that estimate time parameters, provided the interferometers are suitably configured.
If the N baselines in the antenna array of a direction finding interferometer are organized in two-dimensional space, corresponding phase measurements φ are defined by two angles of arrival. In direction finding interferometers with a three-dimensional antenna array, the phase differences cp by extension may depend on three angles of arrival. In such cases, two or three angles of arrival can be estimated on the basis of the phase measurements φ performed on N baselines. Generally, some interferometers can estimate M partial sought parameters ΘP1 . . . ΘPM by processing the N phase measurements φ1 . . . φN on N measuring scales (N>M), in which case equation (16) may be re-written in vector form according to:
φ+k=AΘ+n, (22)
where φ, k, and n are N-dimensional column vectors with every ith element corresponding to an ith baseline in the direction finding interferometer. Again, the elements of vector k represent numbers of full cycles lost in the phase measurements taken on corresponding baselines and the elements of vector n represent phase errors associated with the phase measurements φ taken on corresponding baselines. In comparison to equation (16), ΘP now represents an M-dimensional column vector of partial sought parameters ΘP1 . . . ΘPM, while matrix A has dimensions N×M and is composed of column vectors ai that are N-dimensional linearly independent vectors of relatively prime numbers, which are defined by the structure of antenna array of the interferometer.
Non-extended direction finding interferometers may be configured to provide very accurate unambiguous estimates of several angles of arrival in restricted angle sectors. In extended direction finding interferometers, additional direction finding components can be implemented to provide coarse estimation of sought parameters ΘC1 . . . ΘCM to obtain less accurate unambiguous estimates in wider angle sectors. For instance, extended direction finding interferometers can be configured to estimate partial sought parameters ΘP1 and ΘP2 with 0.1° angle accuracy within a 10° angle sectors. Coarse sought parameter estimators included in such interferometers can also calculate coarse sought parameter estimates ΘC1 and ΘC2 with a 2° angle accuracy within 90° angle sectors. Then, by combining the partial sought parameter estimates ΘPi with the coarse sought parameters ΘCi, the direction finding interferometers can produce fine sought parameter estimates ΘF1 and ΘF2 with a 0.1° angle accuracy within 90° angle sectors.
In direction finding interferometers, each partial sought parameter estimate ΘPi, 1≦i<M represents the sine or cosine of angle of arrival. Extended direction finding interferometer will in some cases provide correspondence in dimensions and values between partial sought parameter estimates ΘPi and coarse sought parameter estimates ΘCi, 1≦i<M. For example, while the partial sought parameter ΘPi is limited to the range −0.5≦ΘPi<0.5, the coarse sought parameter estimate ΘCi may be any real number that is not generally restricted to the same range. But within the limited range −0.5≦ΘPi<0.5, the coarse sought parameter estimate ΘCi and the partial sought parameter estimate ΘPi will correspond to the same physical quantity, e.g. an AOA.
For instance, in some embodiments, the extended direction finding interferometer produces a coarse estimate of AOA in a wide angle sector and a partial sought parameter estimate ΘPi corresponding to a narrow angle sector. In that case, the coarse sought parameter estimator can obtain the coarse sought parameter estimate ΘCi as coarse estimate of AOA divided by the size of the narrow angle sector in degrees. If such correspondence is achieved, then M partial sought parameter extenders 4110 can be used for producing M fine sought parameter estimates ΘFi, 1≦i<M. The M fine sought parameter estimates ΘFi will have the same accuracy as the corresponding partial sought parameter estimates ΘPi, but will represent AOA in the wide-angle sector in which the coarse estimate of angle of arrival is defined. However, it should be appreciated that in different embodiments of direction finding interferometers, the coarse sought parameter estimates ΘCi can be defined differently, provided correspondence in maintained between ΘPi and ΘCi in terms of both dimension and value.
In some embodiments, extended phase interferometer 1130 (
In addition, it should be appreciated that the various elements defined in equation (22) are not restricted only to representing time parameters or angles of arrival. In some embodiments, still other interferometric systems not explicitly described herein may be designed to estimate one or more different interferometric parameters by representing the one or more interferometric parameters using a vector Θ of sought parameters and solving equation (22). Regardless of the physical meaning of the one or more interferometric parameters, if represented by a vector Θ of sought parameters, equation (22) may be solved as described in more detail below to estimate the one or more interferometric parameters.
Referring now to
In some embodiments of the interferometric location system 1100, more than one fine sought parameter estimate ΘFi may be calculated by the extended interferometer 1130 by processing the N phase measurements φ1 . . . φN. In such embodiments, more than one coarse sought parameter estimates ΘCi may also be calculated by a corresponding number of coarse sought parameter estimators 1150 (
As seen in
In
As seen in
The M pre-processed partial sought parameter estimates Θ′Pi are provided to a corresponding number of partial sought parameter extenders 5110 included in the combined estimator 5400. Each of the partial sought parameter extenders 5110 also receives a corresponding one of M coarse sought parameter estimates ΘCi, and generates one of the M fine sought parameter estimates Θ′Fi based on the received partial sought parameter estimate Θ′Pi and coarse sought parameter estimate ΘCi. The M fine sought parameter estimates Θ′F1, . . . , Θ′FM are output from the fine parameter estimator 5000.
In some embodiments, the postprocessor 5300 (
Similarly, in some embodiments, the combined estimator 5400 (
As will be appreciated, the combined estimators 4100 and 4200 shown in
How the combined estimators 4100 and 4200 calculate one partial sought parameter estimate ΘP (or alternatively the combined estimators 5100, 5200 and 5400 estimate the vector Θ of M partial sought parameters) and the one or more noise parameters, as well as how the post-processor 4300 calculates one post-processed fine sought parameter estimate Θ′F (or alternatively the postprocessor 5300 calculates the M post-processed fine sought parameter estimates Θ′F1 . . . Θ′FM, or the preprocessor within combined estimator 5400 calculates the M pre-processed combined partial sought parameter estimates Θ′Pi, 1≦i<M) are now discussed.
Equation (22) may be solved to determine the vector Θ of partial sought parameters on the assumption that vector n is a Gaussian random vector with covariance matrix B. Then a maximum likelihood estimate of the vector Θ of partial sought parameters can be found as the estimate that maximizes the likelihood function:
where T is a multiplier that depends on the covariance matrix B.
For a fixed vector k, the quadratic form in equation (23) is minimized if:
Θ=(ATB−1A)−1ATB−1(φ+k). (24)
The vector k can be found by minimizing the following quadratic form:
where C is a matrix defined by vector A and matrix B according to:
C=B−1−B−1A(ATB−1A)−1ATB−1. (26)
Each of the described interferometers of an interferometer has a specific set of vectors k that shall be considered in equation (25). From this set, N−M linearly independent vectors k1, . . . , kN−M can be chosen in the way that they provide N−M lowest values of
di=kiTCki (27)
Those vectors found from equation (27) can be combined in matrix K, which has dimensions N×(N−M), according to:
K=(k1,k2, . . . , kN−M). (28)
Characteristic matrix S with dimensions N×N can be obtained by combining matrices K and A as follows:
S=(KA). (29)
Matrix S is used in various embodiments of the methods described herein in the effective estimation of the vector Θ of sought parameters and noise parameters. Matrix S has a property that det(S)=±1.
Equation (24) can be rewritten as:
Θ=(ATB−1A)−1ATB−1SS−1(φ+k), (30)
or equivalently as:
Θ=HS−1(φ+k), (31)
where H is a matrix defined by matrices A and B as:
H=(ATB−1A)−1 ATB−1S (32)
In turn, matrix S−1 can be partitioned into two matrices:
where U is a matrix comprised of the first (N−M) row vectors of S−1 according to:
and where V is a matrix comprised of the last M row vectors of S−1 according to:
Accordingly, S−1φ can be partitioned into a vector δ given by:
δ=Uφ, (36)
and a vector ψ given by:
ψ=Vφ. (37)
Any N-dimensional vector k in equation (30) can be represented as a linear combination of the column-vectors from matrix S according to:
k=e1k1+e2k2+ . . . +e(N−M)k(N−M)+e(N−M+1)a1+ . . . +eNaM, (38)
where each of the ei in equation (38) are integers. Also, as will be appreciated:
S−1S=SS−1=I. (39)
Taking into consideration equations (29), (31), (37), (38) and (39), the part of equation (31) can be written as:
Matrix H can be partitioned into two matrices as:
H=(RI), (41)
where R is an M×(N−M)-dimensional matrix of real numbers, and I is the M×M-dimensional identity matrix.
If there are no phase errors in the measurements (n=0), conducted by the interferometer, or alternatively if phase errors are small, and k is a vector that minimizes the quadratic form in equation (25), it can be assumed that:
where O is the (N−M)-dimensional zero vector. According to equations (31), (40), (41), and (42), the vector Θ of sought parameters equals to:
The elements of the vector Θ of sought parameters are bounded by the limits: −0.5≦Θi<0.5. Thus, ej in equation (43) can be eliminated and equation (43) can be rewritten as:
Θ=ψ−rnd[ψ], (44)
where rnd[ . . . ] is a procedure of rounding to the nearest integer every element of a vector inside of the square brackets [ . . . ]. Equation (44) can also be rewritten as:
Θ=rrni{ψ}, (45)
where rrni{ . . . } is a procedure of calculating the residual of rounding to the nearest integer every element of a vector inside of the braces { . . . }.
The accuracy of Θ calculated according to equation (45) can be very sensitive to the level of phase errors. Accordingly, in various embodiments, the level of phase errors, or the noise parameters, which are related to the level of phase errors, are utilized as “quality parameters” or parameters that characterize the quality of Θ. In various embodiments, noise parameters are estimated through the use of matrix U. Equations (29), (34) and (39) indicate that U projects φ and k in a space orthogonal to the column vectors of A. Vectors δ, expressed in equation (36), and χ, where:
χ=Uk, (46)
are (N−M)-dimensional vectors in space orthogonal to A. Any χ is a point of a lattice in . The quadratic form in equation (25) describes Voronoi regions with χ being the center.
Reference is now made to
U(φ+k)=δ−χ. (47)
Moreover, the center of the Voronoi region 6311 that is closest to δ can be approximately estimated as:
χ=rnd[δ]. (48)
In various embodiments, the rounding region 6312 is used instead of Voronoi region 6311, and equation (47) can be written as:
v=rrni{δ}. (49)
Considering the ideal case when there are no phase errors, implying n=0, then: φ=φ0, φ+k=AΘ, and δ=χj in for any Θ. Consequently, if vector v≠0, it is a projection of an N-dimensional error vector n on orthogonal to A. Any N-dimensional vector n can be represented as a sum of components lying in where column vectors ai from matrix A are allocated, and components in that are orthogonal to A. The procedure of projecting n onto excludes components allocated in from the result of the projection, and it leaves components in that are the elements of v. Thus, vector v is defined by phase errors only, and in some embodiments it is used in the estimation of noise parameters along with estimation of Θ.
Reference is now made to
Reference is now made to
In some embodiments, the whole vector v is not inputted into the postprocessor. In some such embodiments, the combined estimator can output a noise parameter, which in some embodiments is calculated as the length of vector v. This parameter α is related to the length of noise vector n and in various embodiments is used as a parameter that indicates how noisy is the estimate of Θ. The noise parameter a can be calculated according to:
Reference is now made to
In some embodiments, alternative methods are used to estimate a noise parameter. For example, in some embodiments, a noise parameter is estimated by detecting whether or not v is out of the (N−M) dimensional parallelotope with center at χ (48), and with sizes defined by thresholds 0≦γij<0.5. Reference is again made to
A vector of Z noise parameters ε can be obtained by comparing vi with Z thresholds corresponding to Z parallelotopes, as in the following:
where v in equation (51) is a logical disjunction, and |vi| in equation (52) is an absolute value of vi. Noise parameter q can be calculated according to:
q=count[ε], (53)
where count [ . . . ] is a procedure of counting number of elements of the binary vector in the square brackets that are a logical “1”, obtained as shown for example in equation (52). If every, γij<γi(j+1), then q shows the number of largest parallelotope with v outside of it. Thus noise parameter q shows how far vector v is from the center of rounding region 6312.
Reference is now made to
Reference is now made to
In various embodiments, both vectors v and ψ are utilized during the estimation of Θ, according to:
Θ=rrni{Hξ}, (54)
where ξ is a vector combination of vectors v and ψ as follows:
In various embodiments, the accuracy of Θ calculated according to equation (54) is less sensitive to the phase errors than the accuracy of Θ when calculated according to equation (45).
Reference is now made to
In various embodiments, a is sent to a postprocessor (e.g., 5300 in
In various embodiments, the ambiguity of the phase measurement is resolved correctly and Θ is calculated without abnormal errors when equation (54) is utilized, and corresponding δ is inside of the right rounding region 6312, as illustrated by the dashed lines, in
Reference is next made to
Reference is now made to
As can be seen from
Reference is now made to
In various embodiments, the use of equation (54) can be suboptimal, because it determines whether the vector v is inside of rounding region 6312 as opposed to whether the vector v is inside of Voronoi region 6311. Referring back to
In various embodiments, as a result of the lack of complete correspondence between the Voronoi region 6311 and the rounding region 6312, some samples of δ calculated by equation (36) and processed according to equations (49) and (54) produce the sought parameters with abnormally high errors due to incorrect ambiguity resolution. This can be illustrated with vector δ1 in
where P is a matrix defined as:
P=KTCK, (58)
and where χi are vectors which form Voronoi region 6311 with center at χ=0. Equation (57) corresponds to:
where η is a vector defined as:
η=Pv. (60)
Voronoi region 6311 can have up to 2(2N−M−1) sides. Vectors χi, defining these sides and χ=0 shall be considered in equation (59). Therefore, the number of χi to estimate them in equation (59) is not more than (2N−M+1−1). Such χi has only 0 and ±1 in its elements and, therefore, every χiTη equation (59) is a linear combination of corresponding elements of η. As far as set of χi forming Voronoi region 6311 for particular matrix A are predefined, it also predefines the set of linear combinations of corresponding elements of η to be considered in equation (59). The magnitudes of 0.5(χiTPχi) are predefined constants, which do not depend on the phase measurements. In various embodiments, these conditions make a combined estimator designed based on the minimization procedure according to equation (59) more effective and efficient than a combined estimator that is designed around a computational procedure that is based on equation (25), especially given that equation (59), while more efficient given the above conditions, is nonetheless, in terms of the final estimate that is produced in the end, equivalent to equation (25).
After the searching of χ* according to equation (59) is performed, the vector Θ of sought parameters can be estimated according to:
Θ=rrni{Hτ}, (61)
where τ is a vector combination of ρ and ψ according to:
and where ρ is given by:
ρ=v+χ*. (63)
Alternatively the vector Θ of sought parameters can be estimated according to:
Θ=rrni{Hξ+f}, (64)
where f is a vector given by:
f=Rχ*, (65)
and where R is a part of matrix Has defined in equation (41).
Reference is next made to
Reference is next made to
In various embodiments, given that equations (61) or (64) completely correspond to the maximum likelihood principle of estimation of Θ, the probability of correct ambiguity resolution for an combined estimator that is designed based on the use of either of these equations is greater than the probability of correct ambiguity resolution for a combined estimator that is designed based on the use of equation (54). For example,
Reference is next made to
Reference is next made to
Reference is next made to
Reference is now made to
Reference is next made to
Reference is now made to
Reference is again made to
Some embodiments and some applications may require a high level of Θ accuracy, very high probability of correct ambiguity resolution, and high interferometer throughput. Accordingly, in some embodiments, the combined estimator can work in an adaptive manner to reduce the amount of computation required and thereby also reduce the amount of time required. In particular, in some embodiments, the combined estimator makes a decision regarding the level of noise and which algorithm is most suitable given the level of noise. In some embodiments, the least computationally intensive algorithm or the equation that is most efficient but still applicable given the level of noise is selected. In other embodiments, any of the applicable equations are selected.
For example, in some embodiments, the discrete noise parameter q can be calculated and a determination of position of v with respect to 2 threshold parallelotopes in If v is inside of the smallest parallelotope and if q=0, then Θ can be estimated according to equation (45). However, if v is outside of the smallest parallelotope, but is inside of the second parallelotope and if q=1, then Θ can be estimated according to equation (54). Also, if v is out of the biggest parallelotope and if q=2, then Θ can be estimated according to equation (61) or (64).
Alternatively, assuming a larger number of parallelotopes is defined, if v is inside of a range of the smallest parallelotopes, so that q is below or equal to a first threshold value (i.e., q≦T1), then Θ can be estimated according to equation (45). However, if v is outside of the range of smallest parallelotopes, but is inside of a range of intermediate parallelotopes, so that q is below or equal to a second threshold value larger than the first threshold value (i.e., T1<q≦T2), then Θ can be estimated according to equation (54). Also, if v is outside of the range of intermediate parallelotopes, so that q is larger than the second threshold value (i.e., T2<q), then Θ can be estimated according to equation (61) or (64).
Reference is now made to
The various embodiments of interferometers described herein can be implemented in hardware, in software running on microprocessor, ASIC, or in combination of hardware and software.
Various systems, apparatus and methods have been described according to example embodiments of the invention, including at least one example of each claimed embodiment. None of the above-described embodiments is limiting in any way, and the claimed embodiments may cover systems, apparatus and methods, as well as aspects thereof, which were not explicitly described above. The claimed embodiments are not limited to systems, apparatus and methods having all of the features of any one example system, apparatus or method described above, or to common features shared by two or more of the systems, apparatus and methods described above. It is possible that a system, apparatus, or method described above does not directly relate to a claimed embodiment of the invention.
While the above description provides example embodiments, it will be appreciated that some features and/or functions of the described embodiments may be susceptible to modification without departing from the scope or operating principles of the described embodiments. What has been described above is intended to be non-limiting and merely illustrative of the invention, the scope of which is defined only by the claims appended hereto.
Claims
1. An interferometer for estimating at least one interferometric parameter of one or more signals received from a source, the interferometer comprising:
- at least one phase measurement module configured to determine a plurality of phase measurements of the one or more signals received from a source;
- at least one coarse sought parameter estimator configured to determine at least one coarse sought parameter representing the at least one interferometric parameter by processing the one or more signals received from the source;
- a fine sought parameter estimator configured to process the at least one coarse sought parameter received from the at least one coarse sought parameter estimator, using the plurality of phase measurements received from the at least one phase measurement module, to determine at least one fine sought parameter representing the at least one interferometric parameter with greater accuracy than the at least one coarse sought parameter.
2. The interferometer of claim 1, wherein the fine sought parameter estimator comprises:
- a combined estimator configured to determine at least one partial sought parameter, representing the interferometric parameter over a narrower range of values than the at least one coarse sought parameter, and at least one noise parameter associated with the plurality of phase measurements by processing the plurality of phase measurements received from the at least one phase measurement module; and
- at least one partial sought parameter extender configured to calculate the at least one fine sought parameter using the at least one partial sought parameter received from the combined estimator and the at least one coarse sought parameter received from the at least one coarse sought parameter estimator.
3. The interferometer of claim 2, wherein the combined estimator is configured to estimate a vector Θ of M partial sought parameters and the at least one noise parameter by processing a vector φ of N phase measurements received into the combined estimator, where N is greater than M, each element of the vector φ of phase measurements defined within one phase cycle, and the vector φ of phase measurements is related to the vector Θ of partial sought parameters by: a vector of N integer numbers k of phase cycles missed in the N phase measurements φ, a vector n of N phase errors associated with the N phase measurements φ, and a matrix A with dimensions N×M comprising M column vectors ai that are N-dimensional linearly independent vectors of relatively prime numbers.
4. The interferometer of claim 3, wherein the vector φ of phase measurements is related to the vector Θ of partial sought parameters according to:
- φ=AΘ−k+n.
5. The interferometer of claim 4, wherein one or more of the at least one partial sought parameter extenders is configured to calculate a corresponding fine sought parameter ΘF according to: where ΘP represents a corresponding partial sought parameter received from the combined estimator, and Θ*C is calculated by processing a corresponding coarse sought parameter ΘC, received from the coarse sought parameter estimator, according to: Θ C * = { Θ CR; if ( Δ Θ C - Θ P ) ≤ 0.5 Θ CR + 1; if ( Δ Θ C - Θ P ) > 0.5 Θ CR - 1; if ( Δ Θ C - Θ P ) < - 0.5, where |X| is an absolute value of X, and where ΘCR represents an integer component of the corresponding coarse sought parameter ΘC defined according to: where rnd[... ] is a procedure for rounding an element inside the square brackets [... ] to a nearest integer, and where ΔΘC represents a residual component of the corresponding coarse sought parameter ΘC defined according to: where rrni{... } is a procedure for calculating a residual of rounding the element inside the braces {... } to the nearest integer.
- ΘF=Θ*C+ΘP,
- ΘCR=rnd[ΘC],
- ΔΘC=rrni{ΘC},
6. The interferometer of claim 4, wherein the combined estimator comprises: where V is a matrix with dimensions M×N that is predefined for the matrix A; where U is a matrix with dimensions (N−M)×N that is predefined for the matrix A; and where rrni{... } is a procedure for calculating residuals of rounding each element of the vector inside the braces {... } to nearest integers.
- a first phase measurements converter configured to calculate an M-dimensional vector ψ by processing the vector cp of phase measurements, received from the at least one phase measurement module, according to: ψ=Vφ,
- a second phase measurements converter configured to calculate an (N−M) dimensional vector δ by processing the vector φ of phase measurements, received from the at least one phase measurement module, according to: δ=Uφ,
- a noise parameters calculator configured to process the vector δ received from the second phase measurements converter to calculate an (N−M) dimensional vector v of noise parameters according to: v=rrni{δ},
7. The interferometer of claim 6, wherein the combined estimator further comprises a partial sought parameters estimator configured to determine the vector Θ of partial sought parameters by processing the vector ψ, received from the first phase measurements converter, according to: wherein the vector Θ of partial sought parameters and the at least one noise parameter are outputs of the combined estimator.
- Θ=rrni{ψ},
8. The interferometer of claim 7, wherein the vector v of noise parameters is an output of the combined estimator.
9. The interferometer of claim 7, wherein the combined estimator further comprises a common noise parameter estimator configured to calculate a common noise parameter a according to: α = ( ∑ i = 1 N - M v i 2 ) 1 / 2, where each vi is an element of the vector v of noise parameters received from the noise parameters calculator, and wherein the common noise parameter α is an output of the combined estimator.
10. The interferometer of claim 7, wherein the combined estimator further comprises a discrete noise parameter estimator having the vector v of noise parameters received from the noise parameters calculator and (N−M)×Z threshold values γij as inputs, the discrete noise parameter estimator configured to calculate: β ij = { 1, v i ≥ γ ij 0, v i < γ ij; i = 1, … ( N - M ), j = 1, … Z, where |vi| is an absolute value of vi, and wherein the discrete noise parameter estimator is configured to calculate Z elements of a vector ε according to: where V is a logical disjunction, and wherein the discrete noise parameter estimator is configured to calculate a discrete noise parameter q according to: where count[... ] is a procedure for counting a number of logical ones in the binary vector inside the square brackets, and wherein the discrete noise parameter q is an output of the combined estimator.
- εj=(β1jvβ2jv... vβ(N−M)j); j=1,... Z,
- q=count[ε],
11. The interferometer of claim 7, wherein the fine sought parameter estimator further comprises a postprocessor configured to generate at least one postprocessed fine sought parameter by processing the at least one fine sought parameter received from the at least one partial sought parameter extender, using the at least one noise parameter received from the combined estimator, to improve an estimate of the at least one interferometric parameter.
12. The interferometer of claim 6, wherein the combined estimator further comprises a partial sought parameters estimator configured to determine the vector Θ of partial sought parameters according to: where H is a matrix with dimensions M×N that is predefined for the matrix A and for a covariance matrix B that characterizes the vector n of phase errors, and ξ is an N-dimensional vector combination of the vector v of noise parameters received from the noise parameters calculator, and the vector ψ received from the first phase measurements converter, according to: ξ = ( v ψ ), wherein the vector Θ of partial sought parameters is an output of the combined estimator.
- Θ=rrni{Hξ},
13. The interferometer of claim 12, wherein the at least one noise parameter is an output of the combined estimator.
14. The interferometer of claim 13, wherein the vector v of noise parameters is an output of the combined estimator.
15. The interferometer of claim 13, wherein the combined estimator further comprises a common noise parameter estimator configured to calculate a common noise parameter α according to: α = ( ∑ i = 1 N - M v i 2 ) 1 / 2, where each vi is an element of the vector v of noise parameters received from the noise parameters calculator, and wherein the common noise parameter α is an output of the combined estimator.
16. The interferometer of claim 13, wherein the combined estimator further comprises a discrete noise parameter estimator having the vector v of noise parameters received from the noise parameters calculator and (N−M)×Z threshold values γij as inputs, the discrete noise parameter estimator configured to calculate: β ij = { 1, v i ≥ γ ij 0, v i < γ ij; i = 1, … ( N - M ), j = 1, … Z, where |vi| is an absolute value of vi, and wherein the discrete noise parameter estimator is configured to calculate Z elements of a vector ε according to: where V is a logical disjunction, and wherein the discrete noise parameter estimator is configured to calculate a discrete noise parameter q according to: where count[... ] is a procedure for counting a number of logical ones in the binary vector inside the square brackets, and wherein the discrete noise parameter q is an output of the combined estimator.
- εj=(β1jvβ2jv... vβ(N−M)j); j=1,... Z,
- q=count[ε],
17. The interferometer of claim 13, wherein the fine sought parameter estimator further comprises a postprocessor configured to generate at least one postprocessed fine sought parameter by processing the at least one fine sought parameter received from the at least one partial sought parameter extender, using the at least one noise parameter received from the combined estimator, to improve an estimate of the at least one interferometric parameter.
18. The interferometer of claim 6, wherein the combined estimator further comprises: where P is a matrix with dimensions (N−M)×(N−M) that is predefined for the matrix A and for a covariance matrix B that characterizes the vector n of phase errors; χ * = arg min χ ( 0.5 ( χ i T P χ i ) + χ i T η ), where each χi is an (N−M)-dimensional vector comprising elements of 0 or ±1 that is predefined for the matrix A and for the covariance matrix B; where H is a matrix with dimensions M×N that is predefined for the matrix A and for the covariance matrix B, and τ is an N-dimensional vector combination of the vector ρ received from the noise parameters corrector, and the vector ψ received from the first phase measurements converter, according to: τ = ( ρ ψ ),
- a noise parameters converter configured to process the vector v of noise parameters received from the noise parameters calculator to calculate an (N−M)-dimensional vector η according to: η=Pv,
- a region shift calculator configured to process the vector η received from the noise parameters converter to calculate an (N−M)-dimensional vector χ* according to:
- a noise parameters corrector configured to process the vector v of noise parameters received from the noise parameters calculator and the vector χ* received from the region shift calculator to calculate an (N−M)-dimensional vector ρ according to: ρ=v+χ*; and
- a partial sought parameters estimator configured to calculate the vector Θ of the partial sought parameters according to: Θ=rrni{Hτ},
- wherein the vector Θ of partial sought parameters is an output of the combined estimator.
19. The interferometer of claim 18, wherein the at least one noise parameter is an output of the combined estimator.
20. The interferometer of claim 19, wherein the vector v of noise parameters is an output of the combined estimator.
21. The interferometer of claim 19, wherein the combined estimator further comprises a common noise parameter estimator configured to calculate a common noise parameter α according to: α = ( ∑ i = 1 N - M v i 2 ) 1 / 2, where each vi is an element of the vector v of noise parameters received from the noise parameters calculator, and wherein the common noise parameter α is an output of the combined estimator.
22. The interferometer of claim 19, wherein the combined estimator further comprises a discrete noise parameter estimator having the vector v of noise parameters received from the noise parameters calculator and (N−M)×Z threshold values γij as inputs, the discrete noise parameter estimator configured to calculate: β ij = { 1, v i ≥ γ ij 0, v i < γ ij; i = 1, … ( N - M ), j = 1, … Z, where |vi| is an absolute value of vi, and wherein the discrete noise parameter estimator is configured to calculate Z elements of a vector ε according to: where V is a logical disjunction, and wherein the discrete noise parameter estimator is configured to calculate a discrete noise parameter q according to: where count[... ] is a procedure for counting a number of logical ones in the binary vector inside the square brackets, and wherein the discrete noise parameter q is an output of the combined estimator.
- εj=(β1jvβ2jv... vβ(N−M)j); j=1,... Z,
- q=count[ε],
23. The interferometer of claim 19, wherein the fine sought parameter estimator further comprises a postprocessor configured to generate at least one postprocessed fine sought parameter by processing the at least one fine sought parameter received from the at least one partial sought parameter extender, using the at least one noise parameter received from the combined estimator, to improve an estimate of the at least one interferometric parameter.
24. The interferometer of claim 6, wherein the combined estimator further comprises: where P is a matrix with dimensions (N−M)×(N−M) that is predefined for the matrix A and for a covariance matrix B that characterizes the vector n of phase errors; χ * = arg min χ ( 0.5 ( χ i T P χ i ) + χ i T η ), where each χi is an (N−M)-dimensional vector comprising elements of 0 or ±1 that is predefined for the matrix A and for the covariance matrix B; where R is a matrix with dimensions M×(N−M) that is predefined for the matrix A and for the covariance matrix B; and where H is a matrix with dimensions M×N that is predefined for the matrix A and for the covariance matrix B, and ξ is an N-dimensional vector combination of the vector v of noise parameters received from the noise parameters calculator, and the vector ψ received from the first phase measurements converter, according to: ξ = ( v ψ ), wherein the vector Θ of partial sought parameters is an output of the combined estimator.
- a noise parameters converter configured to process the vector v of noise parameters received from the noise parameters calculator to calculate an (N−M)-dimensional vector η according to: η=Pv,
- a region shift calculator configured to process the vector η received from the noise parameters converter to calculate an (N−M)-dimensional vector χ* according to:
- a noise parameters corrector configured to process the vector χ* received from the region shift calculator to calculate an M-dimensional vector f according to: f=Rχ*,
- a partial sought parameters calculator configured to calculate the vector Θ of partial sought parameters by processing the vector f, received from the noise parameters corrector, according to: Θ=rrni{Hξ+f},
25. The interferometer of claim 24, wherein the at least one noise parameter is an output of the combined estimator.
26. The interferometer of claim 25, wherein the vector v of noise parameters is an output of the combined estimator.
27. The interferometer of claim 25, wherein the combined estimator further comprises a common noise parameter estimator configured to calculate a common noise parameter a according to: α = ( ∑ i = 1 N - M v i 2 ) 1 / 2, where each vi is an element of the vector v of noise parameters received from the noise parameters calculator, and wherein the common noise parameter α is an output of the combined estimator.
28. The interferometer of claim 25, wherein the combined estimator further comprises a discrete noise parameter estimator having the vector v of noise parameters received from the noise parameters calculator and (N−M)×Z threshold values γij as inputs, the discrete noise parameter estimator configured to calculate: β ij = { 1, v i ≥ γ ij 0, v i < γ ij; i = 1, … ( N - M ), j = 1, … Z, where |vi| is an absolute value of vi, and wherein the discrete noise parameter estimator is configured to calculate Z elements of a vector ε according to: where V is a logical disjunction, and wherein the discrete noise parameter estimator is configured to calculate a discrete noise parameter q according to: where count[... ] is a procedure for counting a number of logical ones in the binary vector inside the square brackets, and wherein the discrete noise parameter q is an output of the combined estimator.
- εj=(β1jvβ2jv... vβ(N−M)j); j=1,... Z,
- q=count[ε],
29. The interferometer of claim 25, wherein the fine sought parameter estimator further comprises a postprocessor configured to generate at least one postprocessed fine sought parameter by processing the at least one fine sought parameter received from the at least one partial sought parameter extender, using the at least one noise parameter received from the combined estimator, to improve an estimate of the at least one interferometric parameter.
30. The interferometer of claim 6, wherein the combined estimator further comprises: β ij = { 1, v i ≥ γ ij 0, v i < γ ij; i = 1, … ( N - M ), j = 1, … Z, where |vi| is an absolute value of vi, and wherein the discrete noise parameter estimator is configured to calculate Z elements of a vector ε according to: where V is a logical disjunction, and wherein the discrete noise parameter estimator is configured to calculate a discrete noise parameter q according to: where count[... ] is a procedure for counting a number of logical ones in the binary vector inside the square brackets; and
- a discrete noise parameter estimator having the vector v of noise parameters received from the noise parameters calculator and (N−M)×Z threshold values γij as inputs, the discrete noise parameter estimator configured to calculate:
- εj=(β1jvβ2jv... vβ(N−M)j); j=1,... Z,
- q=count[ε],
- an adaptive estimator having the discrete noise parameter q received from the discrete noise parameter estimator, the vector v of noise parameters received from the noise parameters calculator, and the vector ψ received from the first phase measurements converter as inputs, the adaptive estimator configured to determine the vector Θ of sought parameters differently based upon the value of the discrete noise parameter q.
31. The interferometer of claim 30, wherein the adaptive estimator is configured to determine the vector Θ of partial sought parameters: ξ = ( v ψ ); and χ * = arg min χ ( 0.5 ( χ i T P χ i ) + χ i T η ), τ = ( ρ ψ ), wherein the vector Θ of partial sought parameters is an output of the combined estimator.
- if q is below or equal to a first threshold, according to: Θ=rrni{ψ};
- if q is above the first threshold and below or equal to a second threshold greater than the first threshold, according to: Θ=rrni{Hξ},
- where H is a matrix with dimensions M×N that is predefined for the matrix A and for a covariance matrix B that characterizes the vector n of phase errors, and ξ is an N-dimensional vector combination of v and ψ according to:
- if q is above the second threshold, by calculating an (N−M)-dimensional vector η according to: η=Pv,
- where P is a matrix with dimensions (N−M)×(N−M) that is predefined for the matrix A and for the covariance matrix B, and by further calculating an (N−M)-dimensional vector χ* according to:
- where each χi is an (N−M)-dimensional vector comprising elements of 0 or ±1 that is predefined for the matrix A and for the covariance matrix B, and by further calculating an (N−M)-dimensional vector ρ according to: ρ=v+χ*,
- and by calculating the vector Θ of partial sought parameters according to: Θ=rrni{Hτ},
- where τ is an N-dimensional vector combination of ρ and ψ according to:
32. The interferometer of claim 31, wherein the discrete noise parameter q is an output of the combined estimator, and wherein the fine sought parameter estimator further comprises a postprocessor configured to generate at least one postprocessed fine sought parameter by processing the at least one fine sought parameter received from the at least one partial sought parameter extender, using the discrete noise parameter q received from the combined estimator, to improve an estimate of the at least one interferometric parameter.
33. The interferometer of claim 30, wherein the adaptive estimator is configured to determine the vector Θ of partial sought parameters: ξ = ( v ψ ); and χ * = arg min χ ( 0.5 ( χ i T P χ i ) + χ i T η ), wherein the vector Θ of partial sought parameters is an output of the combined estimator.
- if q is below or equal to a first threshold, according to: Θ=rrni{ψ};
- if q is above the first threshold and below or equal to a second threshold greater than the first threshold, according to: Θ=rrni{Hξ},
- where H is a matrix with dimensions M×N that is predefined for the matrix A and for a covariance matrix B that characterizes the vector n of phase errors, and ξ is an N-dimensional vector combination of v and ψ according to:
- if q is above the second threshold, by calculating an (N−M)-dimensional vector η according to: η=Pv,
- where P is a matrix with dimensions (N−M)×(N−M) that is predefined for the matrix A and for the covariance matrix B, and by further calculating an (N−M)-dimensional vector χ* according to:
- where each χi is an (N−M)-dimensional vector comprising elements of 0 or ±1 that is predefined for the matrix A and for the covariance matrix B, and by further calculating an M-dimensional vector f according to: f=Rχ*,
- where R is a matrix with dimensions M×(N−M) that is predefined for the matrix A and for the covariance matrix B, and by calculating the vector Θ of sought parameters according to: Θ=rrni{Hξ+f},
34. The interferometer of claim 33, wherein the discrete noise parameter q is an output of the combined estimator, and wherein the fine sought parameter estimator further comprises a postprocessor configured to generate at least one postprocessed fine sought parameter by processing the at least one fine sought parameter received from the at least one partial sought parameter extender, using the discrete noise parameter q received from the combined estimator, to improve an estimate of the at least one interferometric parameter.
35. The interferometer of claim 2, wherein the combined estimator is configured to:
- compare the at least one noise parameter with at least one threshold; and
- based on a result of the comparison, determine the at least one partial sought parameter by processing the plurality of phase measurements.
36. The interferometer of claim 1, wherein the fine sought parameter estimator comprises: where k represents a vector of N integer numbers of phase cycles missed in the N phase measurements φ, n represents a vector of N phase errors associated with the N phase measurements φ, and A represents a matrix with dimensions N×M comprising M column vectors ai that are N-dimensional linearly independent vectors of relatively prime numbers, the combined estimator comprising:
- a combined estimator configured to determine a vector Θ of M partial sought parameters, representing the at least one interferometric parameter over a narrower range of values than the at least one coarse sought parameter, by processing a vector φ of N phase measurements received into the combined estimator, where N is greater than M, each element of the vector φ of phase measurements defined within one phase cycle, and the vector φ of phase measurements is related to the vector Θ of partial sought parameters according to: φ=AΘ−k+n,
- a phase measurements converter configured to calculate an M-dimensional vector ψ by processing the vector φ of phase measurements, received from the at least one phase measurement module, according to: ψ=Vφ,
- where V is a matrix with dimensions M×N that is predefined for the matrix A; and a partial sought parameters estimator configured to process the vector p received from the phase measurements converter to determine the vector Θ of partial sought parameters according to: Θ=rrni{ψ},
- wherein the vector Θ of partial sought parameters is an output of the combined estimator; and
- at least one partial sought parameter extender configured to calculate the at least one fine sought parameter using the at least one partial sought parameter received from the combined estimator and the at least one coarse sought parameter received from the at least one coarse sought parameter estimator.
37. The interferometer of claim 36, wherein one or more of the at least one partial sought parameter extender is configured to calculate a corresponding fine sought parameter ΘF according to: where ΘP represents a corresponding partial sought parameter received from the combined estimator, and Θ*C is calculated by processing a corresponding coarse sought parameter ΘC, received from the coarse sought parameter estimator, according to: Θ C * = { Θ CR; if ( Δ Θ C - Θ P ) ≤ 0.5 Θ CR + 1; if ( Δ Θ C - Θ P ) > 0.5 Θ CR - 1; if ( Δ Θ C - Θ P ) < - 0.5, where |X| is an absolute value of X, and where ΘCR represents an integer component of the corresponding coarse sought parameter ΘC defined according to: where rnd[... ] is a procedure for rounding an element inside the square brackets [... ] to a nearest integer, and where ΔΘC represents a residual component of the corresponding coarse sought parameter ΘC defined according to: where rrni{... } is a procedure for calculating a residual of rounding the element inside the braces {... } to the nearest integer.
- ΘF=Θ*C+ΘP,
- ΘCR=rnd[ΘC],
- ΔΘC=rrni{ΘC},
38. A fine sought parameter estimator for use in an interferometer to estimate at least one interferometric parameter, the fine sought parameter estimator comprising a processor configured to:
- receive a vector φ of N phase measurements and a vector ΘC of M coarse sought parameters;
- estimate a vector Θ of M partial sought parameters by processing the vector φ of phase measurements, where N is greater than M, each element of the vector φ of phase measurements defined within one phase cycle, and the vector φ of phase measurements related to the vector Θ of partial sought parameters by: a vector of N integer numbers k of phase cycles missed in the N phase measurements φ, a vector n of N phase errors associated with the N phase measurements φ, and a matrix A with dimensions N×M comprising M column vectors ai that are N-dimensional linearly independent vectors of relatively prime numbers; and
- process the vector Θ of M partial sought parameters and the vector ΘC of M coarse sought parameters to generate a vector ΘF of M fine sought parameters representing the at least one interferometric parameter with greater accuracy than the vector ΘC of M coarse sought parameters and over a greater range of values than the vector Θ of M partial sought parameters.
39. A method of estimating at least one interferometric parameter of one or more signals from a source, the method comprising:
- determining a plurality of phase measurements of the one or more signals received from a source;
- determining at least one coarse sought parameter representing the at least one interferometric parameter by processing the one or more signals received from the source; and
- processing the at least one coarse sought parameter using the plurality of phase measurements to determine at least one fine sought parameter representing the at least one interferometric parameter with greater accuracy than the at least one coarse sought parameter.
Type: Application
Filed: Feb 17, 2011
Publication Date: Aug 25, 2011
Inventor: Vladimir Slastion (Toronto)
Application Number: 13/029,857
International Classification: G06F 15/00 (20060101);