Abstract: A computerized machine (a) determines temporal and spatial confidence intervals for each one of plural sonic events, (b) classifies pairings among the sonic events as either comparable or non-comparable, and (c) estimates a minimum number of sonic sources, some of which are in motion, that could have produced or generated the sonic events. Sonic event times and positions are characterized by corresponding temporal and spatial confidence intervals. A pairing of sonic events is classified as comparable only when that pairing meets one or more preselected constraints, some of which depend on the temporal and spatial confidence intervals. The estimated minimum number of sonic sources is equal to the total number of sonic events minus the cardinality of a maximum matching of a bipartite graph derived from the classifications of the pairings and a chronological ordering of the set of sonic events.

Abstract: A computerized machine (a) determines temporal and spatial statistical characterizations for each one of plural sonic events, (b) classifies certain pairings among the sonic events as comparable, and (c) estimates a minimum number of sonic sources, some of which are in motion, that could have generated the sonic events. Sonic event times and positions can be characterized by corresponding temporal and spatial confidence intervals. A pairing of sonic events is classified as comparable only when that pairing meets one or more preselected constraints, some of which depend on the temporal and spatial statistical characterizations. The estimated minimum number of sonic sources is equal to the number of sonic events in a longest antichain within a chronological ordering of the set of sonic events. An antichain comprises a subset of the sonic events for which no pairing of sonic events of that subset is a comparable pairing.

Abstract: A method implemented using a programmed computerized machine includes using an estimated uncorrected time-difference-of-arrival (TDOA) for a first signal at first and second sensors, located at first and second sensor locations and coupled to first and second clocks, to calculate a first estimated clock synchronization error (CSE) range that extends (i) from an estimated minimum true TDOA between the sensor locations minus an estimated maximum uncorrected TDOA, (ii) to an estimated maximum true TDOA between the sensor locations minus an estimated minimum uncorrected TDOA. Additional estimated CSE ranges based on additional signals can be used to calculate an estimated cumulative CSE range. A first or cumulative estimated CSE range can be used for calculations with respect to time-dependent signals arriving at one or both sensors, or for adjusting one or both clocks.

Abstract: Multistatic active coherent sonar systems and methods include reception by floating receiver sonobuoys of acoustic signals emitted by floating source sonobuoys, by both direct propagation from the source sonobuoys and reflection or scattering from a target object. Subsequent calculations based at least in part on those signals can be employed to estimate relative or absolute positions or velocities of the target object and the source and receiver sonobuoys. The estimated relative velocities and positions can be calculated without relying on GPS or other extrinsic positioning signals acquired for each sonobuoy after its deployment. Acoustic signals emitted by a stationary source on the seabed, received by a stationary receiver on the seabed, or reflected/scattered from a bathymetric feature, can be employed to estimate absolute or relative positions or velocities of the target object and the source and receiver sonobuoys.

Abstract: Computerized sequential bounded estimation is performed on time-series data. Robust methods use bounds and probability distributions to estimate target parameters for time-dependent data, including but not limited to the location of objects or phenomena. Realistic prior probability distributions of pertinent variables are utilized, and time-dependent measurements and errors in measurements are received. Bounds and probability distributions can be obtained without making any assumption of linearity. The sequential methods used for location are applicable in other applications in which a function of the probability distribution is desired for variables that are related to measurements.

Abstract: Robust methods are developed to provide bounds and probability distributions for the locations of objects as well as for associated variables that affect the accuracy of the location such as the positions of stations, the measurements, and errors in the speed of signal propagation. Realistic prior probability distributions of pertinent variables are permitted for the locations of stations, the speed of signal propagation, and errors in measurements. Bounds and probability distributions can be obtained without making any assumption of linearity. The sequential methods used for location are applicable in other applications in which a function of the probability distribution is desired for variables that are related to measurements.

Abstract: Robust methods are developed to provide bounds and probability distributions for the locations of objects as well as for associated variables that affect the accuracy of the location such as the positions of stations, the measurements, and errors in the speed of signal propagation. Realistic prior probability distributions of pertinent variables are permitted for the locations of stations, the speed of signal propagation, and errors in measurements. Bounds and probability distributions can be obtained without making any assumption of linearity. The sequential methods used for location are applicable in other applications in which a function of the probability distribution is desired for variables that are related to measurements.

Abstract: Robust methods are developed to provide bounds and probability distributions for the locations of objects as well as for associated variables that affect the accuracy of the location such as the positions of stations, the measurements, and errors in the speed of signal propagation. Realistic prior probability distributions of pertinent variables are permitted for the locations of stations, the speed of signal propagation, and errors in measurements. Bounds and probability distributions can be obtained without making any assumption of linearity. The sequential methods used for location are applicable in other applications in which a function of the probability distribution is desired for variables that are related to measurements.

Abstract: Robust methods are developed to provide bounds and probability distributions for the locations of objects as well as for associated variables that affect the accuracy of the location such as the positions of stations, the measurements, and errors in the speed of signal propagation. Realistic prior probability distributions of pertinent variables are permitted for the locations of stations, the speed of signal propagation, and errors in measurements. Bounds and probability distributions can be obtained without making any assumption of linearity. The sequential methods used for location are applicable in other applications in which a function of the probability distribution is desired for variables that are related to measurements.

Abstract: Robust methods are developed to provide bounds and probability distributions for the locations of objects as well as for associated variables that affect the accuracy of the location such as the positions of stations, the measurements, and errors in the speed of signal propagation. Realistic prior probability distributions of pertinent variables are permitted for the locations of stations, the speed of signal propagation, and errors in measurements. Bounds and probability distributions can be obtained without making any assumption of linearity. The sequential methods used for location are applicable in other applications in which a function of the probability distribution is desired for variables that are related to measurements.

Abstract: Robust methods are developed to provide bounds and probability distributions for the locations of objects as well as for associated variables that affect the accuracy of the location such as the positions of stations, the measurements, and errors in the speed of signal propagation. Realistic prior probability distributions of pertinent variables are permitted for the locations of stations, the speed of signal propagation, and errors in measurements. Bounds and probability distributions can be obtained without making any assumption of linearity. The sequential methods used for location are applicable in other applications in which a function of the probability distribution is desired for variables that are related to measurements.

Abstract: Robust methods are developed to provide bounds and probability distributions for the locations of objects as well as for associated variables that affect the accuracy of the location such as the positions of stations, the measurements, and errors in the speed of signal propagation. Realistic prior probability distributions of pertinent variables are permitted for the locations of stations, the speed of signal propagation, and errors in measurements. Bounds and probability distributions can be obtained without making any assumption of linearity. The sequential methods used for location are applicable in other applications in which a function of the probability distribution is desired for variables that are related to measurements.

Abstract: Robust methods are developed to provide bounds and probability distributions for the locations of objects as well as for associated variables that affect the accuracy of the location such as the positions of stations, the measurements, and errors in the speed of signal propagation. Realistic prior probability distributions of pertinent variables are permitted for the locations of stations, the speed of signal propagation, and errors in measurements. Bounds and probability distributions can be obtained without making any assumption of linearity. The sequential methods used for location are applicable in other applications in which a function of the probability distribution is desired for variables that are related to measurements.

Abstract: Robust methods are developed to provide bounds and probability distributions for the locations of objects as well as for associated variables that affect the accuracy of the location such as the positions of stations, the measurements, and errors in the speed of signal propagation. Realistic prior probability distributions of pertinent variables are permitted for the locations of stations, the speed of signal propagation, and errors in measurements. Bounds and probability distributions can be obtained without making any assumption of linearity. The sequential methods used for location are applicable in other applications in which a function of the probability distribution is desired for variables that are related to measurements.

Abstract: A detection problem is introduced for a source of some bandwidth and unknown waveform and emission time in the presence of noise of uncertain variance. The signal travels to the receivers along paths whose delays may be unknown. Using a new receiver called a “matched-lag filter,” the presence or absence of the signal is estimated from the auto- and cross-correlation functions of the received signals. The use of correlation functions provides the first stage of gain in signal-to-noise ratio, like a matched filter, because the paths are assumed to be partially coherent with one another. The second stage achieves additional gain by searching only over the physically possible arrangements of signals in the auto- and cross-correlation functions. These stages enable the matched-lag filter to behave like a matched filter within a matched filter. In a simple case, simulations of the matched-lag filter yield probabilities of detection that are, with one and two receivers, 4.