Deep learning has been shown to be a powerful tool in array processing. Sparse array reconfigurability can be an integral part of cognitive sensing in dynamic radio frequency (RF) environments. In this respect, fast-switching that avoids hardware complexity, insertion loss, and crosstalk distortion is paramount to a realizable perception-action cycle. In this paper, we design sparse arrays using binary switching per RF chain for optimum beamforming that maximizes signal-to-interference-and-noise ratio (SINR). We apply two binary switching strategies and examine their achievable sparse array classification rates and SINR using convolutional neural networks as well as the less complex structure of multilayer perceptron.

We present a robust copula model for landmine detection based on a likelihood ratio test. The test is applied to radar-based imagery from multiple viewpoints of the interrogation area. Different copula density functions are investigated in terms of their effectiveness in incorporating the statistical dependence between multi-view images. The test is designed to maximize the worst-case performance over all feasible mine and clutter distributions. Using numerical radar data of shallow buried targets under varying surface roughness, we demonstrate that the robust copula-based detector outperforms existing approaches and provides a high detection performance for a wide range of false-alarm rates.

We propose a robust likelihood-ratio test (LRT) to detect landmines and unexploded ordnance using forward-looking ground-penetrating radar. Instead of modeling the distributions of the target and clutter returns with parametric families, we construct a band of feasible probability densities under each hypothesis. The LRT is then devised based on the least favorable densities within the bands. This detector is designed to maximize the worst case performance over all feasible density pairs and, hence, does not require strong assumptions about the clutter and noise distributions. The proposed technique is evaluated using electromagnetic field simulation data of shallow-buried targets. We show that, compared to detectors based on parametric models, robust detectors can lead to significantly reduced false alarm rates, particularly in cases where there is a mismatch between the assumed model and the true distributions.

Sampling the physical-array autocorrelations is the initial processing step in standard direction-of-arrival (DoA) estimation with coprime arrays. These samples are then organized into an autocorrelation matrix estimate of a uniform-linear virtual coarray, which in turn is used for DoA estimation. Existing autocorrelation-sampling approaches provide the exact coarray autocorrelation matrix for asymptotically large sample support; however, they attain arbi-trary/suboptimal mean-squared estimation error (MSE) for lim-ited/low sample support. In this work, we present a minimum- MSE (MMSE) approach for autocorrelation sampling. The proposed method offers a superior autocorrelation-matrix estimate that can attain higher DoA estimation accuracy than the standard counterparts.

We present an image-domain adaptive likelihood ratio tests (LRT) detector for low-signature target detection in forward-looking ground-penetrating radar. We exploit multiview tomographic images of the scene under investigation to iteratively adapt the statistics of the targets and clutter arising from the interface roughness. Using numerical electromagnetic data, it is shown that the proposed adaptive LRT detector provides significantly lower false-alarm rates compared with its nonadaptive counterpart while providing comparable detection performance.

Forward-looking ground-penetrating radar (FL-GPR) permits standoff sensing of shallow in-road threats. A major challenge facing this radar technology is the high rate of false alarms stemming from the vulnerability of the target responses to interference scattering arising from interface roughness and subsurface clutter. In this paper, we present a multiview approach for target detection in FL-GPR. Various images corresponding to the different views are generated using a tomographic algorithm, which considers the near-field nature of the sensing problem. Furthermore, for reducing clutter and maintaining high cross-range resolution over the imaged area, each image is computed in a segmentwise fashion using coherent integration over a suitable set of measurements from multiple platform positions. We employ two fusion approaches based on likelihood ratio tests detector to combine the multiview images for enhanced target detection. The superior performance of the multiview approach over single-view imaging is demonstrated using electromagnetic modeling data.

Distributed radar networks for through-the-wall radar imaging (TWRI) have the advantage of flexibility, high accuracy, and fault tolerance. We propose a modified distributed orthogonal matching pursuit (MDOMP) algorithm with an efficient communication scheme for the sparse scene reconstruction in TWRI applications. The communication costs, computational complexity, and reconstruction performance are analyzed for the proposed algorithm and compared with existing distributed sparse reconstruction methods. Simulated and experimental data are used to demonstrate that the MDOMP provides desirable performance at moderate communication and computation costs.

This paper establishes the role of sparse arrays and sparse sampling in antijam global navigation satellite systems (GNSS). We show that both jammer direction of arrival estimation methods and mitigation techniques benefit from the design flexibility of sparse arrays and their extended virtual apertures or coarrays. Taking advantage of information redundancy, significant reduction in hardware and computational cost materializes when selecting a subset of array antennas without sacrificing jammer nulling or localization capabilities. In addition to the spatial array sparsity, antijam can utilize sparsity of jammers in the spatio-temporal frequency domains. By virtue of their finite number, jammers in the field of view are sparse in the azimuth and elevation directions. For the class of frequency modulated jammers, sparsity is also exhibited in the joint time-frequency signal representation. These spatial and signal characteristics have called for the development of sparsity-aware antijam techniques for the accurate estimation of jammer space-time-frequency signature, enabling its effective sensing and excision. Both theory and simulation examples demonstrate the utility of coarrays, sparse reconstructions, and antenna selection techniques for antijam GNSS.

In this paper, we consider multipath exploitation and sparse reconstruction in a network of distributed multistatic radar units for stationary target localization behind walls. Multipath exploitation leverages prior information of the indoor scattering environment to eliminate ghosts targets. However, uncertainties in interior wall positions severely impair the effectiveness of multipath exploitation. We develop a multipath signal model for the distributed radar network configuration, which parameterizes the wall locations, and perform joint optimization for simultaneously recovering the target and wall positions. Supporting simulation results are provided, which validate the effectiveness of the proposed method.

In this paper, we utilize Bayesian Compressive Sensing (BCS) for direction-of-arrival (DOA) estimation based on the coarray. This enables estimation of more sources than the number of physical antennas. We adopt the covariance vectorization technique to construct the received signal vectors of coarrays for both fully and partially augmentable arrays. We then apply the single measurement vector BCS (SMV-BCS) for DOA estimation. Supporting simulation results for both sparse linear arrays and circular arrays demonstrate the effectiveness of the proposed approach in terms of high resolution and estimation accuracy compared to the MUSIC and sparse signal reconstruction based methods.

In this letter, direction-of-arrival (DOA) estimation of a mixture of coherent and uncorrelated targets is performed using sparse reconstruction and active nonuniform arrays. The data measurements from multiple transmit and receive elements can be considered as observations from the sum coarray corresponding to the physical transmit/receive arrays. The vectorized covariance matrix of the sum coarray observations emulates the received data at a virtual array whose elements are given by the difference coarray of the sum coarray (DCSC). Sparse reconstruction is used to fully exploit the significantly enhanced degrees-of-freedom offered by the DCSC for DOA estimation. Simulated data from multiple-input multiple-output minimum redundancy arrays and transmit/receive co-prime arrays are used for performance evaluation of the proposed sparsity-based active sensing approach.

Detection and localization of stationary targets behind walls is primarily challenged by the presence of the overwhelming electromagnetic signature of the front wall in the radar returns. In this paper, we use the discrete prolate spheroidal sequences to represent spatially extended stationary targets, including exterior walls. This permits the formation of a linear block sparse model relating the range profile and observation vectors. Effective wall clutter suppression can then be performed prior to sparse signal image reconstruction. We consider stepped-frequency radar with two cases of frequency measurement distributions over antenna positions. In the first case, the same subset of frequencies is used for each antenna in physical or synthetic aperture arrays, whereas the other case allows different sets of few frequency observations to be available at different antennas. Using experimental data, we demonstrate that the proposed scheme enables sparsity-based image reconstruction techniques to effectively detect and localize behind-the-wall stationary targets from reduced measurements.

Through-the-wall radar imaging aims at determining the locations and velocities of obscured targets. The slow velocities of indoor targets are in particular difficult to detect and estimate. It is shown by theoretical considerations and simulation that indirect propagation paths contain significant information on the target movements, which can be utilized for improved sensing. We propose a compressive sensing based method that exploits a multipath model to improve the velocity resolution of the reconstruction. Simulation results demonstrate the effectiveness of the proposed approach.

We address the problem of detecting building dominant scatterers using a reduced number of measurements with applications to through-the-wall radar (TWR) and urban sensing. We consider oblique illumination, which specially enhances the radar returns from the corners formed by the orthogonal intersection of two walls. This letter uses a novel type of image descriptor, named correlogram, which encodes information about spatial correlation of complex amplitudes of each TWR image pixel. The proposed technique compares the known correlogram of the scattering response of an isolated canonical corner reflector with the correlogram of the received radar signal. The feature-based nature of the proposed detector enables corner separation from other indoor scatterers, such as humans.

Multipath propagation can create ghost targets that severely affect the reconstruction quality of through-the-wall radar images. We propose a compressive sensing (CS) based reconstruction method, which inverts a specular multipath model through exploitation of the structured sparsity in the scene. This allows suppression of the ghost targets and increased signal-to-clutter ratio at the target locations, leading to `clean' images of stationary scenes. Simulation results demonstrate the effectiveness of the proposed approach.

We consider sparsity-driven change detection (CD) for human motion indication in through-the-wall radar imaging and urban sensing applications. Stationary targets and clutter are removed via CD, which converts a populated scene into a sparse scene of a few human targets moving inside enclosed structures and behind walls. We establish appropriate CD models for various possible human motions, ranging from translational motions to sudden short movements of the limbs, head, and/or torso. These models permit scene reconstruction within the compressive sensing framework. Results based on laboratory experiments show that a sizable reduction in the data volume is achieved using the proposed approach without a degradation in system performance.

Compressive sensing (CS) for urban operations and through-the-wall radar imaging has been shown to be successful in fast data acquisition and moving target localizations. The research in this area thus far has assumed effective removal of wall electromagnetic backscatterings prior to CS application. Wall clutter mitigation can be achieved using full data volume which is, however, in contradiction with the underlying premise of CS. In this paper, we enable joint wall clutter mitigation and CS application using a reduced set of spatial-frequency observations in stepped frequency radar platforms. Specifically, we demonstrate that wall mitigation techniques, such as spatial filtering and subspace projection, can proceed using fewer measurements. We consider both cases of having the same reduced set of frequencies at each of the available antenna locations and also when different frequency measurements are employed at different antenna locations. The latter casts a more challenging problem, as it is not amenable to wall removal using direct implementation of filtering or projection techniques. In this case, we apply CS at each antenna individually to recover the corresponding range profile and estimate the scene response at all frequencies. In applying CS, we use prior knowledge of the wall standoff distance to speed up the convergence of the orthogonal matching pursuit for sparse data reconstruction. Real data are used for validation of the proposed approach.

Multipath signals arise in many active sensing modalities, such as radar and sonar. Moving targets cause Doppler effects that could vary for different paths. Target Doppler information corresponding to direct and non-direct paths is important for moving target localizations and classifications, particularly when narrowband signals are involved. This information, however, becomes difficult to reveal when dealing with nonlinear time-varying multi-component Doppler signals. In this paper, we introduce a new time-frequency analysis technique based on the local phase information to accurately extract complex Doppler signature of each signal arrival. This is achieved through short-time polynomial phase modeling of data segments. The global behavior is obtained by fusion of the phases across neighboring segments using the data phase continuity property. The offering of the proposed technique is demonstrated using synthetic data in an over-the-horizon radar platform.

In this paper, we propose a Gausssian-Rayleigh mixture modeling approach to segment indoor radar images in urban sensing applications. The performance of the proposed method is evaluated on real 2D polarimetric data. Experimental results show that the proposed method enhances image quality by distinguishing between target and clutter regions. The proposed method is also compared to an existing Neyman-Pearson (NP) target detector that has been recently devised for through-the-wall radar imaging. Performance evaluation of both methods shows that the proposed method outperforms the NP detector in enhancing the input images.

High-resolution radar imaging systems employ wideband signals and large array apertures, resulting in the generation of large amounts of data. This renders both data acquisition and processing challenging. In this letter, we propose a hybrid approach for a stepped-frequency imaging radar system, which is based on the compressive sensing and novel concepts of microwave tomography, to establish a reduced-redundancy spatial and frequency measurement configuration. The proposed approach provides clear advantages in terms of measurement time and algorithm complexity while maintaining the quality of the scene reconstructions. Laboratory experiments are used to validate the hybrid strategy.

We establish the multipath model which encompasses the target and its multipath “ghosts” in urban and through-the-wall synthetic aperture radar (SAR). The focused downrange and crossrange locations of multipath ghosts are derived and validated using numerical and experimental data. The multipath model permits an implementation of a multipath exploitation algorithm, which maps each target ghost to its corresponding true target location. In doing so, the proposed algorithm improves the radar system performance by aiding in ameliorating the false positives in the original SAR image as well as increasing the SNR at the target locations, culminating in enhanced behind the wall target detection and localization.

This letter presents experimental verification of a tomographic through-the-wall radar imaging technique by exploiting real data measurements of 3-D scenes carried out under semicontrolled conditions. More specifically, the 3-D reconstructions are obtained by a linear inverse scattering algorithm and a sliced approach. This strategy ensures fast data processing and allows quick investigation of a very large spatial region. The effectiveness of the imaging scheme is shown for both a single-target scene and a scene with multiple targets at different depths.

We derive a multipath model for sensing through walls using radars. The model considers propagation through a front wall and specular reflections at interior walls in an enclosed room under surveillance. The model is derived such that additional eigenrays can be easily accommodated. A synthetic aperture radar (SAR) system is considered, and stationary or slowly moving targets are assumed. The focused downrange and crossrange locations of multipath ghosts are established and validated using numerical, as well as experimental data. The multipath model permits an implementation of a multipath exploitation algorithm, which associates, as well as maps, each target ghost back to its corresponding true target location. In doing so, the proposed algorithm improves the radar system performance by aiding in ameliorating the false positives in the original SAR image, as well as increasing the signal-to-clutter ratio at the target locations, culminating in enhanced behind the wall target detection and localization.

Carrier diverse radars employing two different frequencies, termed as dual-frequency radars, prove effective in determining the target range in urban sensing and through-the-wall applications. In this paper, we derive the maximum likelihood (ML) estimator for the dual frequency radar returns for a micro-Doppler motion profile, which is commonly exhibited by indoor moving targets. Unlike linear models, the respective ML estimator does not have a closed form. We solve the ML estimator for dual frequency radar operations, using iterative reweighted least squares (IRLS). The ML-IRLS algorithm is applied to experimental radar returns for estimating the motion parameters of indoor targets.

Significant multipath propagation and heavy clutter in indoor environments renders through-the-wall radar imaging a difficult and complex proposition. It is highly desirable to properly interpret the radar images and determine the contents of the indoor scene with a high level of confidence. Data collected from multiple positions around a structure can be used to improve imaging visibility into the indoor scene, thereby enhancing indoor target detection and localization. In this paper, we consider multi-location radar imaging. Image fusion techniques for combining radar images acquired from multiple locations along two sides of an enclosed structure are presented. Supporting results, based on real-data, are also provided.

An important task in urban sensing applications is to accurately localize moving and vibrating targets in the presence of significant background clutter. A dual-frequency CW radar, which estimates the range of a target based on the phase difference between two closely spaced frequencies, has been shown to be a cost-effective approach for range estimation of a moving target. Previous work has shown that the use of Fourier transform and time-frequency analysis techniques provides an estimate of Doppler signature and enhanced signal-to-noise ratio (SNR), thereby enabling the range estimation of moving targets and significantly improving the estimation accuracy. In this paper, we consider the combined use of these technologies with a synthetic aperture array for the localization of inanimate moving targets. The synthetic array aperture provides the capability of high-resolution spatial localization of inanimate moving targets, as well as determining the orientation of the rotation or vibration.

Through-the-wall imaging and urban sensing is an emerging area of research and development. The incorporation of the effects of signal propagation through wall material in producing an indoor image is important for reliable through-the-wall mission operations. We have previously analyzed wall effects, such as refraction and change in propagation speed, and designed a wideband beamformer for 2D imaging using line arrays. In this letter, we extend the analysis to 3D imaging via delay-and-sum beamforming in the presence of a single uniform wall. The third dimension provides valuable information on target heights that can be used for enhancing target discrimination/identification. Supporting simulation results are provided.

A dual-frequency radar, which estimates the range of a target based on the phase difference between two closely spaced frequencies, has been shown to be a cost-effective approach to accomplish both range-to-motion estimation and tracking. This approach, however, suffers from two drawbacks: it cannot deal with multiple moving targets, and it has poor performance in noisy environments. In this letter, we propose the use of time-frequency signal representations to overcome these drawbacks. The phase, and subsequently the range information, is obtained based on the moving target instantaneous Doppler frequency law, which is provided through time-frequency signal representations. The case of multiple moving targets is handled by separating the different Doppler signatures prior to phase estimation.

A wideband synthetic aperture radar system based on beamspace Capon beamforming is presented for urban sensing applications. Various effects of signal propagation through building materials are incorporated into the beamformer design. Proof of concept is provided using real data collected in a laboratory environment. Comparison between data-independent and scene-dependent beamformers is provided. The results show that the beamspace Capon beamformer outperforms the nonadaptive delay-and-sum beamformer.

In this paper, statistical bounds on dual-frequency range estimations are provided. Single frequency (Doppler) radars cannot be used in range estimation due to their range ambiguities. An additional frequency can be used to increase the maximum unambiguous range to accepted values for indoor range estimation of moving targets. The dual-frequency approach offers the benefit of reduced complexity, fast computation time, and real time target tracking. Indoor inanimate objects such as fans, vibrating machineries, and clock pendulums exhibit simple harmonic motions, whereas animate translation movements are typically linear. We provide Cramer-Rao bounds for the parameters defining both types of motions and show their dependency on the observation period and partial knowledge of motion and noise parameters.

The quality and reliability of through-the-wall radar imagery is governed, among other things, by the knowledge of the wall characteristics. Ambiguities in wall characteristics smear and blur the image, and also shift the imaged target positions. An autotechnique, b ased on higher order statistics, i s presented which corrects for errors under unknown walls. Simulation results show that the proposed technique provides high-quality focused images with target locations in close proximity to true target positions.

An interactive software written in Visual C# has been developed to provide real-time simulation capabilities for imaging behind the wall scenes. The software implements algorithms and techniques developed by the researchers at the Center for Advanced Communications in Villanova University, but is also amenable to house other imaging approaches. The software features a user friendly and flexible graphical interface that permits easy and interactive scene construction, from specification of the wall and array element locations to placement of objects at various locations behind the wall. All operations are performed using comprehensible dialog boxes and mouse drag-and-drop actions. For illustration, we present an example that demonstrates the usage of the real-time through-the-wall imaging radar simulator. The software serves as an educational tool for courses on radar imaging, introducing the students to the important emerging technology of through-the-wall imaging

A coarray-based synthetic aperture beamformer using stepped-frequency signal synthesis and post-data acquisition processing is presented for wideband imaging of near-field scenes. The proposed beamformer formulation and implementation finds key applications in through-the-wall microwave imaging and landmine detection problems. While coarray techniques offer significant reduction in array elements for a given angular resolution, stepped-frequency realization of wideband systems simplifies implementation and offers flexibility in beamforming. Proof of concept is provided using real data collected in an anechoic chamber.

We derive an analytic expression for the point spread function, using the concept of coarrays, for wideband pulse-echo imaging of targets. We analyze the performance of the imaging system under finite and infinite range conditions. The results provide a better understanding of the interplay between bandwidth, element locations, weighting of the elements, pulse shape within the bandwidth, and imaging performance. This leads to new insights on array and pulse design. We support our results through computer simulations.

We consider imaging arrays and apertures from the point of view of their performance in the imaging of spatially incoherent source distributions. The concept of coarray is used to provide an interesting insight into the aperture synthesis process using frequency diversity imaging. Simulation results are also provided. The proposed methods are applicable to imaging of coherent distribution of reflectors as well.