Abstract: One aspect of this disclosure relates to a method for processing a received, modulated radar pulse to resolve a radar target from noise or other targets. According to an embodiment of the method, a radar return signal is received and samples of the radar return signal are obtained. A minimum mean-square error (MMSE) pulse compression filter is determined for each successive sample. The MMSE filter is separated into a number of components using contiguous blocking, where each component includes a piecewise MMSE pulse compression filter segment. An estimate of radar range profile is obtained from an initialization stage or a previous stage. The piecewise MMSE pulse compression filter segments are applied to improve accuracy of the estimate. The estimate is repeated for two or three stages to adaptively suppress range sidelobes to a level of a noise floor. Other aspects and embodiments are provided herein.

Abstract: An adaptive radar processing system includes an antenna array for transmitting a radar signal and for receiving a return radar signal, and a signal processor programmed with an enhanced FRACTA algorithm (FRACTA.E). The basic FRACTA algorithm is enhanced to FRACTA.E with (any or all of) five enhancements, versions 1-5. Version 1 is a stopping criterion, for censoring samples, that is adaptive to a radar return data set. The inclusion of a stopping criterion improves the computational speed of FRACTA.E thereby improving its efficiency. Version 2 uses global censoring. Version 3 uses fast reiterative censoring. Version 4 uses segmenting of data vectors for AMF application. Version 5 uses Knowledge-aided covariance estimation (KACE) to reduce the required sample support that may be necessary in non-homogeneous environments, providing substantially the same level of detection performance with considerably less training data.

Abstract: An apparatus for non-coherently detecting slow-moving targets in high resolution sea clutter includes a binary detector for converting high resolution radar returns, produced in response to a radar pulse scan of a plurality of identical pulses, into corresponding binary outputs based on a comparison of range cell magnitudes with a detector threshold. A range extent filter converts these binary outputs into an output indicating the presence or absence of a cluster of the returns that are closely spaced in range, while a third, persistence integration stage determines target range extent persistence over a predetermined time period. A detector stage declares detection of a target based on a comparison of the output of the third stage with a selected threshold.

Abstract: A method is provided for detecting a target signal of a specific known form in the presence of clutter. The method includes dividing a set of initial training data, derived from returns from a burst of identical pulses, into a set of censored data and a set of uncensored data. A covariance matrix estimate, based on the uncensored data, is used to compute adaptive coherence estimate values, and an average adaptive coherence estimate threshold level is computed for each Doppler band to obtain a corresponding threshold. The censored data and the covariance matrix estimate are used to compute adaptive coherence estimate values for the uncensored data for each Doppler band, and these values are compared with the respective thresholds to determine the presence or absence of the target signal.

Abstract: A radar receiver system includes a receiver, a processor, and a detector. The processor is programmed with a Multistatic Adaptive Pulse Compression (MAPC) algorithm for estimating adaptively a pulse compression filter, for each range cell of a plurality of range cells, and for each of a plurality of radar return signals, to remove interference between the radar return signals. MAPC may also include reiterative minimum mean-square error estimation for applying to each of the range cells in order to adaptively estimate a unique pulse compression filter for each cell. MAPC adaptively mitigates the masking problem that results from the autocorrelation of a waveform which produces range sidelobes scaled by the target amplitudes as well as the cross-correlation between waveforms. MAPC can also be applied when only 1 or some subset of the available illuminated radar range profiles are desired, with undesired information then discarded.

Abstract: A radar pulse compression repair (RPCR) system includes a receiver for receiving a radar return signal, a matched filter for applying matched filtering to the radar return signal to generate a matched filter output, a processor programmed for applying Radar Pulse Compression Repair (RPCR) to the matched filter output to suppress a plurality of range sidelobes from the matched filter output, and a detector for receiving the RPCR-processed output. The RPCR invention in operating upon the output of the matched filter enables RPCR to be employed as a post-processing stage in systems where it is not feasible to replace the existing pulse compression apparatus. RCPR can also be selectively employed when it is possible that large targets are present that may be masking smaller targets, thereby keeping computational complexity to a minimum.

Abstract: A method for processing a received, modulated pulse (i.e. waveform) that requires predictive deconvolution to resolve a scatterer from noise and other scatterers includes receiving a return signal; obtaining L+(2M?1)(N?1) samples y of the return signal, where y(?)={tilde over (x)}T(?)s+?(?); applying RMMSE estimation to each successive N samples to obtain initial impulse response estimates [{circumflex over (x)}1{?(M?1)(N?1)}, . . . , {circumflex over (x)}1{?1}, {circumflex over (x)}1{0}, . . . , {circumflex over (x)}1{L}, . . . , {circumflex over (x)}1{L?1}, {circumflex over (x)}1{l?1+(M?1)(N?1)}]; computing power estimates {circumflex over (?)}1(?)=|{circumflex over (x)}1(?)|? for ?=?(M?1)(N?1), . . . , L?1+(M?1)(N?1) and 0<??2; computing MMSE filters according to w(?)=?(?) (C(?)+R)?1 s, where ?(?)=E[|x(?)|?] is the power of x(?), for 0<??2, and R=E[?(?) ?H(?)] is the noise covariance matrix; applying the MMSE filters to y to obtain [{circumflex over (x)}2{?(M?2)(N?1)}, . . .

Abstract: A method for processing a received, modulated pulse (i.e. waveform) that requires predictive deconvolution to resolve a scatterer from noise and other scatterers includes receiving a return signal; obtaining L+(2M?1)(N?1) samples y of the return signal, where y(l)={tilde over (x)}T(l)s+?(l); applying RMMSE estimation to each successive N samples to obtain initial impulse response estimates [{circumflex over (x)}1{?(M?1)(N?1)}, . . . ,{circumflex over (x)}1{?1},{circumflex over (x)}1{0}, . . . ,{circumflex over (x)}1{L?1},{circumflex over (x)}1{L}, . . . ,{circumflex over (x)}1{L?1+(M?1)(N?1)}]; computing power estimates {circumflex over (?)}1(l)=|{circumflex over (x)}1(l)|2 for l=?(M?1)(N?1), . . . ,L?1+(M?1)(N?1); computing MMSE filters according to w(l)=?(l)(C(l)+R)?1s, where ?(l)=|x(l)|2 is the power of x(l), and R=E[v(l)vH(l)] is the noise covariance matrix; applying the MMSE filters to y to obtain [{circumflex over (x)}2{?(M?2)(N?1)}, . . . ,{circumflex over (x)}2{?1},{circumflex over (x)}2{0}, . . .