USA As Represented by Secretary of the Navy

University of Kansas

United States of America As Represented by the Secretary of the Air Force

Shannon D Blunt

Karl Robert Gerlach

Justin G Metcalf

Gerald Brandon Ravenscroft

Erik S Perrins

Cenk Sahin

Aaron K Shackelford

Tsz Ping Chan

Kevin J Smith

Jonathan William Owen

Lumumba Harnett

Patrick M Mccormick

Mihai Popescu

Thomas Higgins

Patrick M McCormick

Systems and methods of embodiments provide a feasible approach to implementing Far-Field Radiated Emission Design (FFRED) techniques suitable for simultaneous transmission of radar and communication signals. A set of signals for transmission and a transmission direction for each signal of the set of signals may be determined. The set of signals includes at least a first signal associated with a first transmission direction and a second signal associated with a second transmission direction that is different from the first direction. An optimization problem is configured based on characteristics of an antenna array and the set of signals and then solved to identify a set of waveforms suitable for transmitting the signals. The set of waveforms may include at least two waveforms, each of the at least two waveforms configured for transmission by a different antenna element of the antenna array. The determined waveforms may be coherent in the far-field and suitable for power efficient transmission.

Physical waveform optimization for multiple-beam multifunction digital arrays

Systems, methods, and computer-readable storage media for generating, transmitting, and utilizing a composite radar and communication waveform are disclosed. The composite radar and communication waveform may facilitate radar detection and data communication operations and may be generated from a frequency modulated (FM) radar waveform and a communication signal. In an aspect, the composite radar and communication waveform may be generated by iteratively executing a shaping process against the FM radar waveform and the communication signal until a first stop criterion is satisfied to produce an initial composite radar and communication waveform having the communication signal embedded therein, and then iteratively executing an enhancement process against the initial composite radar waveform and the communication signal until a second stop criterion is satisfied to produce a final composite radar and communication waveform suitable for both radar detection and data communication operations.

Power-efficient formulation of tandem-hopped radar and communications

The present application discloses a new form of μ-STAP, referred to herein as post μ-STAP or Pμ-STAP, which overcomes the drawbacks associated with existing μ-STAP techniques. The Pμ-STAP techniques described herein facilitate the generation of additional training data and homogenization after pulse compression. For example, Pμ-STAP techniques may apply a plurality of homogenization filters to a pulse compressed datacube generated from an input radar waveform, which produces a plurality of new pulse compressed datacubes with improved characteristics. Unlike existing μ-STAP techniques described above, which require pre-pulse compressed data to operate, the Pμ-STAP techniques disclosed in the present application are designed to utilize pulse compressed data, and therefore may be readily applied to legacy radar systems.

Method to improve ground moving target detection through partially adaptive post pulse compression multi-waveform space-time adaptive processing

An ad hoc approach denoted as devoid clutter capture and filling (DeCCaF) that addresses the nonstationarity effects that arise when input radar waveform returns exhibiting dynamic spectra variations are processed to combat dynamic RFI is disclosed. Portions of the spectra of each input waveform return of a set of input radar waveform returns processed during the CPI may be filled with clutter information borrowed from other waveform returns of the set of waveform returns. DeCCaF may combined with an appropriate filter (e.g., a matched filter, a mismatched filter) to achieve results that are nearly indistinguishable from input radar waveform returns in which no spectral variation are present.

Devoid clutter capture and filling (deccaf) to compensate for intra-CPI spectral notch variation

Performing medical imaging. The generation of medical images, which includes a Source AFFine Image REconstruction (SAFFIRE) algorithm, is based on an iterative implementation of minimum mean-square error (MMSE) estimation within an affine-transformed solution space and utilizes a matched filter bank initialization coupled with energy normalization of each successive estimate. An incoherent integration technique provides an alternative implementation strategy to either increase signal-to-noise ratio (SNR) or generalize the estimator to accommodate temporally-separated interference sources. In addition, the estimator solution may be employed to determine volumetric constraints with which to re-apply the estimator to further improve the estimation accuracy.

Source affine reconstruction for medical imaging

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.

Computationally efficient adaptive radar pulse compression system

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

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.

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Shawnee, KS, United States of America

Shannon D Blunt