Abstract: The present disclosure generally pertains to systems and methods for processing radar signals from a sparse MIMO array. In some embodiments, the signals from a MIMO radar array are processed to generate a sparse virtual array. Then, by using a two-dimensional (2D) variant of missing-data iterative adaptive approach (missing-data IAA or MIAA) to process the virtual array, the system can estimate information from the missing antennas of the sparse virtual array. Then, by using the now full virtually array, the system can process the virtual array using a variant of multi-dimensional folding (MDF) to discover the existence and location (e.g., distance, elevation, and azimuth) of objects (also called scatterers) within the MIMO radar array's field of view.

Abstract: The present disclosure generally pertains to systems and methods for processing radar signals from a sparse MIMO array. In some embodiments, the signals from a MIMO radar array are processed to generate a sparse virtual array. Then, by using a two-dimensional (2D) variant of missing-data iterative adaptive approach (missing-data IAA or MIAA) to process the virtual array, the system can estimate information from the missing antennas of the sparse virtual array. Then, by using the now full virtually array, the system can process the virtual array using a variant of multi-dimensional folding (MDF) to discover the existence and location (e.g., distance, elevation, and azimuth) of objects (also called scatterers) within the MIMO radar array's field of view.

Abstract: Embodiments of a device and a frequency data extrapolator are generally described herein. The frequency data extrapolator may receive input frequency data mapped to a two-dimensional frequency grid. As an example, the input frequency data may be based on return signals received, at a sensor of the device, in response to pulsed transmissions of the sensor in a physical environment. Regions of the frequency grid may be classified as high fidelity or low fidelity. A group of basis rectangles may be determined within the high fidelity regions. A column-wise extrapolation matrix and a row-wise extrapolation matrix may be determined based on the input frequency data of the basis rectangles. The input frequency data of the high fidelity regions may be extrapolated to replace the input frequency data of the low fidelity regions.

Abstract: Embodiments of a device and a frequency data extrapolator are generally described herein. The frequency data extrapolator may receive input frequency data mapped to a two-dimensional frequency grid. As an example, the input frequency data may be based on return signals received, at a sensor of the device, in response to pulsed transmissions of the sensor in a physical environment. Regions of the frequency grid may be classified as high fidelity or low fidelity. A group of basis rectangles may be determined within the high fidelity regions. A column-wise extrapolation matrix and a row-wise extrapolation matrix may be determined based on the input frequency data of the basis rectangles. The input frequency data of the high fidelity regions may be extrapolated to replace the input frequency data of the low fidelity regions.

Abstract: A bistatic synthetic aperture radar (SAR) imaging system and method include: combining each radar return pulse from airborne radar platforms with a sinusoid; deskewing each reduced radar return pulse; estimating motion parameters based on a maximum likelihood estimation (MLE); performing MLE motion correction to generate motion-corrected radar return pulses; acquiring position and velocity estimates of the airborne radar platforms and scattering locations; defining bistatic range and velocity vectors; defining new bistatic range and velocity vectors in a new set of orthogonal axes; projecting vector distance differences between the radar scattering locations along the new set of orthogonal axes to generate new range and velocity measurements along the new set of orthogonal axes; converting the new range and velocity measurements to map Doppler frequency into cross-range; and forming a bistatic SAR image in range and cross-range based on cross-range extent derived from the Doppler frequency mapping.

Abstract: A synthetic aperture radar imaging method that combines each radar return pulse with a sinusoid to reduce the radar return pulses to a baseband frequency and deskew each radar return pulse. It includes determining a maximum likelihood estimate (MLE) of residual motion parameters for a dominant scatterer on the ground relative to the airborne radar and correcting for errors in inertial navigation system measurements based on the MLE residual motion parameters. It includes convolving each radar return pulse with its corresponding radar transmission pulse to generate a range compressed image for each radar return pulse and generating a sub-band range profile image for each radar return pulse and its corresponding radar transmission pulse based on the corresponding range compressed image that has been corrected for residual motion. Performing bandwidth extrapolation on each sub-band and subsequently combining the three bands to produce an enhanced resolution image without grating lobes.

Abstract: A system and method for discrimination and identification of a target including: receiving a radar return signal including target information and clutter information; determining a two-fold forward or forward-backward data matrix from the received signal, using a multi-dimensional folding (MDF) process; computing singular values of the two-fold forward or forward-backward data matrix; using the computed singular values to determine a noise power level of the radar return signal; determining the number of scatterers in the radar return signal according to a predetermined threshold value above the noise power; estimating complex Doppler and azimuth frequencies of each scatterer from the determined number of scatterers using the MDF process; determining dispersive scatterers and non-dispersive scatterers using the estimated Doppler and azimuth complex frequencies of each scatterer; and distinguishing the target information from the clutter information, according to the determined dispersive scatterers and non-di

Abstract: Embodiments of synthetic aperture radar systems for mapping Doppler frequency to cross-range to form bistatic inverse synthetic radar images of airborne targets.

Abstract: A method of target discrimination and identification, on a computer including a processing unit and a non-volatile storage device, from a radar signal having a plurality of radar return signals, is presented. The method includes: modeling, on the computer, the radar return signals by linear prediction to produce linear prediction equations; solving, on the computer, the linear prediction equations by the Burg algorithm to produce linear prediction coefficients for a linear prediction coefficient polynomial; computing, on the computer, roots of the linear prediction coefficient polynomial to produce scattering modes; computing, on the computer, a distance of each of the scattering modes to a unit circle; computing, on the computer, a complex envelope for each mode of the scattering modes; and selecting, on the computer, target scattering modes from among the scattering modes based on the distance of the mode to the unit circle and the complex envelope of the mode.

Abstract: System and method for calculating three dimensional residual motion errors of a moving platform with respect to a point of interest by receiving a radar signal from the point of interest (302); forming a radar image including a plurality of scatterers (304); using an MLE method to obtain range, radial velocity and acceleration of the moving platform for a first peak scatterer in the radar image (306); correcting a location of the first peak scatterer with respect to a scene center of the point of interest (312); updating the obtained radial acceleration responsive to the corrected location (314); and updating the obtained radial velocity of the moving platform responsive to the updated radial acceleration (316).

Abstract: A system and method for discrimination and identification of a target including: receiving a radar return signal including target information and clutter information; determining a two-fold forward or forward-backward data matrix from the received signal, using a multi-dimensional folding (MDF) process; computing singular values of the two-fold forward or forward-backward data matrix; using the computed singular values to determine a noise power level of the radar return signal; determining the number of scatterers in the radar return signal according to a predetermined threshold value above the noise power; estimating complex Doppler and azimuth frequencies of each scatterer from the determined number of scatterers using the MDF process; determining dispersive scatterers and non-dispersive scatterers using the estimated Doppler and azimuth complex frequencies of each scatterer; and distinguishing the target information from the clutter information, according to the determined dispersive scatterers and non-di

Abstract: System and method for calculating three dimensional residual motion errors of a moving platform with respect to a point of interest by receiving a radar signal from the point of interest (302); forming a radar image including a plurality of scatterers (304); using an MLE method to obtain range, radial velocity and acceleration of the moving platform for a first peak scatterer in the radar image (306); correcting a location of the first peak scatterer with respect to a scene center of the point of interest (312); updating the obtained radial acceleration responsive to the corrected location (314); and updating the obtained radial velocity of the moving platform responsive to the updated radial acceleration (316).

Abstract: A method of target discrimination and identification, on a computer including a processing unit and a non-volatile storage device, from a radar signal having a plurality of radar return signals, is presented. The method includes: modeling, on the computer, the radar return signals by linear prediction to produce linear prediction equations; solving, on the computer, the linear prediction equations by the Burg algorithm to produce linear prediction coefficients for a linear prediction coefficient polynomial; computing, on the computer, roots of the linear prediction coefficient polynomial to produce scattering modes; computing, on the computer, a distance of each of the scattering modes to a unit circle; computing, on the computer, a complex envelope for each mode of the scattering modes; and selecting, on the computer, target scattering modes from among the scattering modes based on the distance of the mode to the unit circle and the complex envelope of the mode.

Abstract: The target detection from a slow moving radar platform technology includes a system. The system includes a radar power determination module configured to determine a clutter power based on radar information associated with a radar signal. The system further includes a maximum likelihood determination module configured to determine a plurality of maximum likelihoods from the radar signal within a plurality of substantially equally spaced frequencies and based on the clutter power. The system further includes a maximum threshold determination module configured to determine a maximum threshold from the plurality of maximum likelihoods and based on the clutter power. The system further includes a target detection module configured to detect the target based on the maximum threshold and a target detection threshold.

Abstract: The target detection from a slow moving radar platform technology includes a system. The system includes a radar power determination module configured to determine a clutter power based on radar information associated with a radar signal. The system further includes a maximum likelihood determination module configured to determine a plurality of maximum likelihoods from the radar signal within a plurality of substantially equally spaced frequencies and based on the clutter power. The system further includes a maximum threshold determination module configured to determine a maximum threshold from the plurality of maximum likelihoods and based on the clutter power. The system further includes a target detection module configured to detect the target based on the maximum threshold and a target detection threshold.

Abstract: A system and method for implementing a maximum likelihood estimator for making a joint estimation of range, range rate, and acceleration of a target utilizing a pulse doppler radar. The MLE of target motion parameters are determined by keystone processing a baseband signal, and generating a first estimate of the motion parameters based on the processed signal. The first estimate is utilized to set up sampling intervals for the performance of a coarse search. Then a fine search is performed using Newton's method to determine the MLE.

Abstract: A system and method for implementing a maximum likelihood estimator for making a joint estimation of range, range rate, and acceleration of a target utilizing a pulse doppler radar. The MLE of target motion parameters are determined by keystone processing a baseband signal, and generating a first estimate of the motion parameters based on the processed signal. The first estimate is utilized to set up sampling intervals for the performance of a coarse search. Then a fine search is performed using Newton's method to determine the MLE.

Abstract: A moving radar generates a search mode synthetic aperture image of a patch having a principal scatterer. The boundaries of the patch are from R0 to R1 slant range and ?0 to ?1 azimuth angle. A computer motion compensates digital samples to obtain a motion compensated digital array. The motion compensated digital array is converted to a frequency array in the frequency domain Kx, Ky The frequency array has a rectangular aperture extending ?Kx and ?Ky. Available samples from the frequency array are computed using a Range Migration Algorithm including a Stolt interpolation. Usable samples are identified from the available samples using one or more criteria. Usable samples are removed from available samples to obtain incomplete samples. Features related to the patch having a principal scatterer are extracted from the usable samples. The features are used to extrapolate extrapolated samples from the usable samples.

Abstract: A radar classifies an unknown target illuminated with a large bandwidth pulse. The large bandwidth pulse may be algorithmically synthesized. The target reflects the large bandwidth pulse to form a return. The return is digitized into digital samples at range bin intervals. A computer extracts unknown range and amplitude pairs descriptive of the unknown target from the digital samples. Some range and amplitude pairs are located within one range bin interval. Principle scatterers are extracted from the unknown range and amplitude pairs using Modified Forward backward linear Prediction to form an unknown feature vector for the target. A plurality of pre-stored, known feature vectors containing known range and amplitude pairs are retrieved from the computer. The known range and amplitude pairs are descriptive of known targets, and are grouped in clusters having least dispersion for each of the known targets.

Abstract: A synthetic aperture radar (SAR) system having a single phase center antenna is provided, the SAR system including a measurement unit and a tracker unit. The measurement unit is capable of receiving a phase history of a target point scatterer. From the phase history, then, the measurement unit is capable of estimating a ground position, velocity and acceleration of the target to thereby detect the target. The tracker unit, in turn, is capable of updating the ground position, velocity and acceleration of the target to thereby track the target based upon the ground position, velocity and acceleration. In this regard, the tracker unit is capable of updating the ground position, velocity and acceleration using a Kalman filter.