USA As Represented by Secretary of the Navy

Other

Science Applications International Corporation

Karl Robert Gerlach

Shannon D Blunt

Michael Picciolo

Frank F Kretschmer, Jr

Jay Scott Goldstein

Grealie A Andrews

Aaron K Shackelford

Michael Lee Picciolo

Kevin J Smith

This invention is an improvement upon the basic adaptive signal processors, the Multi-Stage Wiener Filter and the cascaded canceller. The invention combines the concepts of soft weighting the adaptive weights of either type of processor disclosed herein with reiteratively processing the outputs by returning them to the input of the chosen adaptive signal processor. The combination of these functions improves the Signal to Interference plus Noise Ratio (SINR), the Probability of Detection (Pd), and/or the Bit Error Rate (BER). The invention improves statistical convergence of these types of metrics such that fewer training data samples are needed to achieve a particular satisfactory value of these metrics than would occur using traditional computational adaptive signal processors.

Method and system for soft-weighting a reiterative adaptive signal processor

A single pulse imaging (SPI) radar system for creating a radar image from a plurality of Doppler phase-shifted return radar signals in a radar environment of moving targets includes a transmitter; a receiver for receiving a radar return signal; an analog-to-digital converter (ADC) coupled to the output of the receiver; a processor, coupled to the output of the ADC, that is programmed with an SPI algorithm that includes a bank of range/Doppler-dependent adaptive RMMSE-based filters; and a target detector. The algorithm estimates adaptively a range profile for each of the Doppler phase-shifted return radar signals to create the radar image of the moving targets.

Single pulse imaging radar system and method

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.

Multistatic adaptive pulse compression method and system

A radar receiver system includes a receiver, a processor including a Doppler Compensated Adaptive Pulse Compressor (DCAPC) algorithm, possible other intermediate processing and a target detector. The DCAPC algorithm processes samples of a radar return signal, applies Minimum Mean Square Error (MMSE), or alternatively matched filtering, to the radar return signal to obtain initial radar impulse response estimates, computes power estimates, estimates a range cell Doppler shift for each range cell, computes range-dependent filters, applies the MMSE filters, and then repeats the cycle for subsequent reiterative stages until a desired length&#x2212;L range window is reached, thereby resolving the scatterer from noise and other scatterers.

Doppler-compensated radar pulse compression processing system and method

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.

Radar pulse compression repair

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.

Method and apparatus for detecting slow-moving targets in high-resolution sea clutter

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.

Doppler-sensitive adaptive coherence estimate detector methods

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&#x2013;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.

Radar processor system and method

An adaptive signal processing system utilizes a Multistage Weiner Filter having an analysis section and a synthesis section that includes a processor. The processor includes an algorithm for generating a data adaptive linear transformation, computing an adaptive weighting wof the data, and applying the computed adaptive weighting wto a function of a main input signal and an auxiliary input signal to generate an output signal. A plurality of building blocks in a Gram-Schmidt cascaded canceller-type configuration sequentially decorrelate input signals from each other to produce a single filtered output signal. Each building block generates an adaptive weight wthat is applied to generate a local output signal. The effect of non-Gaussian noise contamination on the convergence MOE of the system is negligible. In addition, when desired signal components are included in weight training data they cause little loss of noise cancellation.

Multistage median cascaded canceller

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&#x2212;1)(N&#x2212;1) samples y of the return signal, where y(l)={tilde over (x)}(l) s+v(l); applying RMMSE estimation to each successive N samples to obtain initial impulse response estimates [{circumflex over (x)}{&#x2212;(M&#x2212;1)(N&#x2212;1)}, . . . , {circumflex over (x)}{&#x2212;1}, {circumflex over (x)}{0}, . . . , {circumflex over (x)}{L&#x2212;1}, . . . , {circumflex over (x)}{L}, {circumflex over (x)}{&#x2212;1 +(M&#x2212;1)(N&#x2212;1)}]; computing power estimates {circumflex over (&#x3c1;)}(l)=|{circumflex over (x)}(l)|for l=&#x2212;(M&#x2212;1)(N&#x2212;1), . . . , L&#x2212;1+(M&#x2212;1)(N&#x2212;1) and 0&#x3c;&#x3b1;&#x2266;2; computing MMSE filters according to w(l)=&#x3c1;(l) (C(l)+R)s, where &#x3c1;(l)=E[|x(l)|] is the power of x(l), for 0&#x3c;&#x3b1;&#x2266;2, and R=E[v(l) v(l)] is the noise covariance matrix; applying the MMSE filters to y to obtain [{circumflex over (x)}{&#x2212;(M&#x2212;2)(N&#x2212;1)}, . . . , {circumflex over (x)}{&#x2212;1}, {circumflex over (x)}{0}, . . . , {circumflex over (x)}{L&#x2212;1}, {circumflex over (x)}{L}, . . . , {circumflex over (x)}{L&#x2212;1+(M&#x2212;2)(N&#x2212;1)}]; and repeating (d)&#x2013;(f) for subsequent reiterative stages until a desired length-L range window is reached, thereby resolving the scatterer from noise and other scatterers. The RMMSE predictive deconvolution approach provides high-fidelity impulse response estimation. The RMMSE estimator can reiteratively estimate the MMSE filter for each specific impulse response coefficient by mitigating the interference from neighboring coefficients that is a result of the temporal (i.e. spatial) extent of the transmitted waveform. The result is a robust estimator that adaptively eliminates the spatial ambiguities that occur when a fixed receiver filter is used.

Robust predictive deconvolution system and method

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&#x2212;1)(N&#x2212;1) samples y of the return signal, where y(l)={tilde over (x)}(l)s+v(l); applying RMMSE estimation to each successive N samples to obtain initial impulse response estimates [{circumflex over (x)}{&#x2212;(M&#x2212;1)(N&#x2212;1)}, . . . , {circumflex over (x)}{&#x2212;1}, {circumflex over (x)}{0}, . . . , {circumflex over (x)}{L&#x2212;1}, {circumflex over (x)}{L}, . . . , {circumflex over (x)}{L&#x2212;1+(M&#x2212;1)(N&#x2212;1)}]; computing power estimates {circumflex over (&#x3c1;)}(l)=|{circumflex over (x)}(l)|for l=&#x2212;(M&#x2212;1)(N&#x2212;1), . . . , L&#x2212;1+(M&#x2212;1)(N&#x2212;1); computing MMSE filters according to w(l)=&#x3c1;(l)(C(l)+R)s, where &#x3c1;(l)=|x(l)|is the power of x(l), and R=E[v(l)v(l)] is the noise covariance matrix; applying the MMSE filters to y to obtain [{circumflex over (x)}{&#x2212;(M&#x2212;2)(N&#x2212;1)}, . . . , {circumflex over (x)}{&#x2212;1}, {circumflex over (x)}{0}, . . . , {circumflex over (x)}{L&#x2212;1}, {circumflex over (x)}{L}, . . . , {circumflex over (x)}{L&#x2212;1+(M&#x2212;2)(N&#x2212;1)}]; and repeating (d)-(f) for subsequent reiterative stages until a desired length-L range window is reached, thereby resolving the scatterer from noise and other scatterers. The RMMSE predictive deconvolution approach provides high-fidelity impulse response estimation. The RMMSE estimator can reiteratively estimate the MMSE filter for each specific impulse response coefficient by mitigating the interference from neighboring coefficients that is a result of the temporal (i.e. spatial) extent of the transmitted waveform. The result is a robust estimator that adaptively eliminates the spatial ambiguities that occur when a fixed receiver filter is used.

An adaptive signal processing system utilizes a pseudo-median cascaded canceller to compute a set of complex adaptive weights and generate a filtered output signal. The system includes a plurality of building blocks arranged in a Gram-Schmidt cascaded canceller-type configuration for sequentially decorrelating input signals from each other to thereby yield a single filtered output signal. Each building block includes a local main input channel which receives a local main input signal, a local auxiliary input channel which receives a local auxiliary input signal, and a local output channel which sends a local filtered output signal. Each building block generates a complex adaptive weight which is the sample median value of the real and imaginary parts of the ratio of local main input weight training data to local auxiliary input weight training data, and each building block generates a local output signal utilizing the complex adaptive weight. The effect of non-Gaussian noise contamination on the convergence MOE of the system is negligible. In addition, when desired signal components are included in weight training data they cause little loss of noise cancellation.

Pseudo-median cascaded canceller

A novel band partitioned noise cancellation method and device is disclosed. In the invention, in which main and auxiliary channel data is transformed to the frequency domain by FFT's and then associated by frequency subbands. The noise is then cancelled using a sequence of operations including a commutating Gram-Schmidt canceller, an equivalent linear weight calculator, formation of a weight matrix, conversion of the weights to time domain by an inverse FFT and rotation of the weights. The weights are then combined with the main and auxiliary channel data in a transversal filter network to produce a noise-cancellation residue.

Systolic multiple channel band-partitioned noise canceller

A method and apparatus are provided for filtering noise from a plurality of input signals in a sensor system. One first main input signal and several first auxiliary input signals are obtained and interior weights corresponding to the first main and auxiliary signals are determined. Thereafter, equivalent linear weights corresponding to the interior weights are determined and the equivalent linear weights are applied to second main and auxiliary input signals to filter the second main and auxiliary signals and produce one final output signal. Alternatively, the first input signals can be a subset of the second input signals such that a large set of input signals can be filtered by calculating weighting factors from a subset thereof.

Slaved Gram Schmidt adaptive noise cancellation method and apparatus

A method and apparatus exploiting the discovery that the crosscorrelation of constantly spaced rows of the matrices representing certain pulse codes sum to zero. In a ranging system, such as a radar, pulses are coded according to the rows of a such a matrix, transmitted sequentially and each return processed sequentially through a filter matched to one of the coded pulses. (A different preselected filter is used for each return.) The sequence of filters is chosen so that for returns for a given range interval, each filter is matched to the returning pulse, resulting in outputs from the filters representing auto-correlations of the returned pulses. These outputs are time delayed added coherently to form the compressed pulse, and annunciated as a target hit. Should the filters and returns be mismatched, as with ambiguous stationary clutter returns, the outputs of the filters are cross-correlations which, according to said discovery, sum to zero. Thus the invention operates to remove ambiguous range clutter from returns in such a ranging system.

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Chesapeake Beach, MD, United States of America

Karl Robert Gerlach