Radio frequency interference (RFI) is a critical problem for ultra-wideband synthetic aperture radars (UWB SAR) because the VHF/UHF band used by them is shared by other systems as well. A number of solutions have been proposed over the years. Recently, sparsity and low-rank estimation-based solutions were shown to perform better than traditional methods such as adaptive notch filtering. These algorithms model the SAR signal to be sparse and the RFI to be either sparse or low-ranked in nature and solve an optimization problem to estimate the SAR signal and RFI simultaneously. Algorithms in this class share the common characteristic that the SAR signal sparsity is captured by modeling each data vector as a linear combination of shifted SAR pulses. This data model addresses the structure of SAR signals in the down-range direction, but the inter-aperture cross-range structure, i.e., the fact that SAR signals add coherently across the cross-range, has been completely ignored. In this work, we incorporate this “global” 2-D structure of the SAR data into the RFI mitigation problem. Two algorithms are proposed: 1) (2-D) sparse SAR and sparse RFI estimation and 2) (2-D) sparse SAR and low-rank RFI estimation. The experimental results demonstrate that the 2-D model does a much better job of capturing the sparsity of SAR and the 2-D algorithms consistently perform better than the “local” 1-D algorithms. The level of improvement rises significantly in challenging cases-when the noise level and/or the number of RFI bands increases. Experiments are conducted extensively on simulated data sets as well as real SAR and RFI data sets collected by the U.S. Army Research Laboratory (ARL) to validate the proposed framework.

An inverse scattering problem (ISP) for synthetic aperture radar (SAR) data in application to through-the-wall imaging is addressed. In contrast with the conventional algorithms of SAR imaging, which work with the linearized mathematical model based on the Born approximation, the fully nonlinear case is considered here. To avoid the local minima problem, the so-called “convexification” globally convergent inversion scheme is applied to approximate the distribution in the slant range (SR) plane of a function that is associated with the dielectric constant. It is demonstrated that this function estimates quite well both the value of the dielectric constant and the location of the target. The benchmark scene of this article comprises a homogeneous dielectric wall and different dielectric targets hidden behind it. The results comprise 2-D images of the SR dielectric constant of the scene of interest. Numerical results are obtained by the proposed inversion method for both the computationally simulated and experimental data. Our results show that the values, cross-range sizes, and locations of SR dielectric constants for targets hidden behind the wall are close to those of real targets. Numerical comparison with the solution of the linearized ISP provided by the Born approximation, commonly used in conventional SAR imaging, shows a significantly better accuracy of our results.

Radio frequency interference (RFI) causes serious problems to ultrawideband (UWB) radar operations due to severely degrading radar imaging capability and target detection performance. This paper formulates proper data models and proposes novel methods for effective RFI mitigation. We first apply the single-snapshot Sparse Iterative Covariance-based Estimation (SPICE) algorithm to data from each pulse repetition interval for RFI mitigation and discuss the connection of SPICE to the l1-penalized least absolute deviation (l1-PLAD) approach. Then, we devise a modified group SPICE algorithm and we prove that it is equivalent to a special case of the l1,2-PLAD method. The modified group SPICE algorithm can be applied to data from a coherent processing interval for effective RFI mitigation. Both the single-snapshot SPICE and the modified group SPICE methods simultaneously exploit the sparsity properties of both RFI spectrum and UWB radar target echoes. Unlike the existing sparsity-based RFI suppression methods, such as the robust principal component analysis algorithm, the proposed methods are hyperparameter-free and therefore easier to use in practical applications. Furthermore, the fast implementation of the SPICE methods is considered by exploiting the special structures of both single-snapshot and multiple-snapshot covariance matrices. Finally, the results obtained from applying the SPICE methods to simulated data as well as measured data collected by the U.S. Army Research Laboratory synthetic aperture radar system are presented to demonstrate the effectiveness of the proposed methods.

This paper presents a novel technique to recover the missing spectral information due to RF spectral restriction in ultra-wideband (UWB) synthetic aperture radar (SAR) imaging. We address a critical problem in UWB radar imaging: radar transmission is prohibited in reserved frequency bands specified by local frequency management agencies. We model the problem in a compressed sensing setup where a time-sparse received radar signal at each aperture is collected in the incoherent frequency domain using a stepped-frequency implementation. Using electromagnetic (EM) finite-difference, time-domain (FDTD) data of various targets and clutter objects computed at all viewing aspect angles, we show that the proposed technique can successfully recover the relevant information on targets of interest even from a large percentage of missing frequency bands.

Multiple-input multiple-output forward-looking ground-penetrating radar (GPR) systems can be used to detect landmines. To enhance its performance, two data-dependent imaging algorithms, based on the sparse learning via iterative minimization (SLIM) and sparse covariance-based estimation (SPICE) techniques for high-resolution imaging, applied to the time-domain GPR data, were previously developed. Time-domain SLIM (TD-SLIM) and time-domain SPICE (TD-SPICE) yield higher resolution and lower sidelobe compared with data-independent approaches such as delay-and-sum and recursive sidelobe minimization. However, the two data-dependent imaging algorithms are computationally expensive. To provide both improved landmine detection performance and significantly reduced computational time compared with the TD-SLIM and TD-SPICE algorithms, we propose herein the frequency-domain SLIM algorithm.

This paper presents a comprehensive benchmark comparison in objective as well as subjective performances of radio-frequency interference (RFI) suppression/extraction techniques for ultra-wideband (UWB) signals in synthetic aperture radar (SAR) imaging applications. In this study, we employ two sets of UWB SAR signals: one simulated from a step-frequency radar setup, whereas the other is collected on the testing field in a real-world setup from the U.S. Army Research Laboratory. Similarly, our RFI experiments involve two RFI data sets: one is simulated from a collection of randomly generated frequency bands and the other is the RFI data collected in a real-world environment with the radar receiving antenna pointing toward Washington DC. These SAR and RFI data sets represent four diverse experimental setups where we can carefully benchmark the denoising performance of several popular RFI-mitigation techniques in the current literature based on notch-filtering, principal component analysis (PCA), model-based sparse recovery, and simultaneous low-rank and sparse recovery or robust PCA (RPCA). We validate that RPCA and model-based sparse recovery consistently yields the best overall RFI separation performance on a wide range of settings in all data sets.

Ground-penetrating radar (GPR) has been used in a number of applications including the detection of landmines and improvised explosive devices. Most existing algorithms for forward looking GPR image reconstruction require that a hyperparameter or regularization parameter be chosen by the user. Selecting these parameters in an optimal fashion is a challenging problem that, in theory, can be addressed using cross validation and the L-curve method. Unfortunately, these hyperparameter selection methods are time consuming and not well suited for real-time applications. In this paper, we propose a hyperparameter-free algorithm that follows from a maximum a posteriori (MAP) formulation where the prior probability density function is obtained by the so-called “integrate-out" approach. First, we model the reflection coefficients as statistically independent random variables that are identically distributed and Laplacian with parameter, λ. Then, we analytically integrate out the parameter, λ, and optimize the resulting MAP objective function using the majorize-minimize optimization technique and soft-thresholding operator. We tested the algorithm using synthetic and real experimental data, and compared the results subjectively and objectively with the results obtained from wellknown algorithms. Although the evaluation is not an exhaustive study, it demonstrates the feasibility of the proposed algorithm.

This paper presents a simple adaptive framework for robust separation and extraction of multiple sources of radio-frequency interference (RFI) from raw ultra-wideband (UWB) radar signals in challenging bandwidth management environments. RFI sources pose critical challenges for UWB systems since (i) RFI often occupies a wide range of the radar's operating frequency spectrum; (ii) RFI might have significant power; and (iii) RFI signals are difficult to predict and model due to their non-stationary nature as well as the complexity of various communication devices. Our proposed framework involves a standard RFI detection step that operates directly on previously-collected contaminated radar signals to identify RFI-dominant frequency sub-bands. This vital information is then applied to construct an RFI dictionary with various sinusoidal patterns spanning these RFI bands. We then employ a sparsity-driven optimization to estimate and then extract RFI from the received radar signals. Our method can be considered as a de-noising preprocessing stage for raw radar signals prior to image formation and other follow-up tasks. Recovery results from extensive simulated as well as real-world UWB synthetic aperture radar (SAR) data sets illustrate the robustness and effectiveness of our framework.

Ultra-wideband (UWB) Synthetic Aperture Radars (SAR) often operate in environments where other systems are present or some of the frequency bands may be reserved for other purposes. In this paper we propose a novel method based on sparse representation to recover SAR signals from radio frequency interference (RFI) and missing spectral information. The new method makes use of correlation between SAR data records in the cross-range direction in addition to the correlation in down-range direction as previously proposed by us[1, 2]. We demonstrate that the proposed method yields robust recovery results in the presence of high levels of RFI (up to 20dB) and up to 75% of frequency bands missing.

Radio-frequency interference (RFI) is the most common, and also the most challenging type of interference or noise source that has a direct impact on the performance of ultrawideband radar systems in various practical application settings. Existing techniques for RFI suppression either employ filtering (notching) which introduces other harmful side-effects such as side-lobe distortion and target-amplitude reduction or RFI modeling/estimation/tracking which requires complicated narrow-band modulation models or even direct RFI sniffing. In this paper, we propose a robust and adaptive technique for the separation and then suppression of RFI signals from ultra-wideband (UWB) radar data via modeling RFI as low-rank components in a joint optimization framework. More specifically, we advocate a joint sparse-and-low-rank recovery approach that simultaneously solves for (i) UWB radar signals as sparse representations with respect to a dictionary containing transmitted waveforms; and (ii) RFI signals as a low-rank structure. The proposed technique is completely adaptive with highly time-varying environments, and does not require any prior knowledge of the RFI sources (other than the low-rank assumption). Both simulated data and real-world data measured by the U.S. Army Research Laboratory (ARL) Ultra-Wideband (UWB) synthetic aperture radar (SAR) confirm that the proposed RFI separation/suppression technique successfully recovers UWB radar signals embedded in large-amplitude RFI signals.

The radar data to form synthetic aperture radar (SAR) imagery is normally transmitted and received by moving platforms like aircraft or vehicles. In many situations, the platforms move at high speed; which reduces the number of sampling records collected to the synthetic aperture, hence degrades the quality of the reconstructed SAR images. Therefore, it is necessary to develop an algorithm that is capable of increasing the temporal frequency rate of the received data. In this paper, we propose a novel technique to generate intermediate records from the existing ones by a locally-adaptive low-rank matrix recovery framework. The system first fills in the blank records using a bi-directional motion estimation scheme. The initialized aperture records are then refined by a robust low-rank matrix completion algorithm using the reference from neighborhood clean records. Experiments demonstrate that the proposed method provides comparative results when up-converting the aperture rate by a factor of two or four, both in mean square error of the raw SAR signal and PSNR performance of the recovered SAR images.

The goal of this paper is to introduce the application of a globally convergent inverse scattering algorithm to estimate dielectric constants of targets using time-resolved backscattering data collected by a U.S. Army Research Laboratory forward-looking radar. The processing of the data was conducted blind, i.e., without any prior knowledge of the targets. The problem is solved by formulating the scattering problem as a coefficient inverse problem for a hyperbolic partial differential equation. The main new feature of this algorithm is its rigorously established global convergence property. Calculated values of dielectric constants are in a good agreement with material properties, which were revealed a posteriori.

In this paper, we propose a novel, robust, and adaptive technique for the extraction of radio frequency interference (RFI) signals from ultra-wideband (UWB) radar data via sparse recovery. Unlike notch-filtering techniques that have been widely employed in the past, our proposed technique directly estimates and suppresses RFI signals from the UWB radar signal directly in time domain. Therefore, it does not suffer from several detrimental side effects such as high-sidelobe distortion and target-amplitude reduction as often observed in notch-filtering approaches. In addition, the technique is completely adaptive with highly time-varying environments and does not assume any knowledge (from frequency band to modulation scheme) of the RFI sources. The proposed technique is based on a sparse-recovery approach that simultaneously solves for (i) the UWB radar signal embedded in RFI noise with large amplitudes and (ii) RFI signals. Using both simulated and real-world data measured by the U.S. Army Research Laboratory (ARL) UWB synthetic aperture radar (SAR), we show that our proposed RFI extraction technique successfully recovers the UWB radar signal embedded in large-amplitude RFI signals. An average of 12 dB of RFI suppression is consistently realized in the real radar data experiments.

In this paper, we propose a novel robust sparse-recovery technique that allows sub-Nyquist uniform under-sampling of wide-bandwidth radar data in real time (single observation). Although much of the information is lost in the received signal due to the low sampling rate, we hypothesize that each wide- bandwidth radar data record can be modeled as a superposition of many backscattered signals from reflective point targets in the scene. In other words, our proposed technique is based on direct sparse recovery via orthogonal matching pursuit using a special dictionary containing many time-delayed versions of the transmitted probing signal. Using data from the U.S. Army Research Laboratory (ARL) Ultra-Wideband (UWB) synthetic aperture radar (SAR), we show that the proposed sparse-recovery model- based (SMB) technique successfully models and synthesizes the returned radar data from real-world scenes using only an analytical waveform that models the transmitted signal and a handful of reflectivity coefficients. More importantly, the reconstructed SAR imagery using the SBM technique with data sampled at only 20% of the original sampling rate has a comparable signal-to-noise ratio (SNR) to the original SAR imagery. For comparison purpose, the paper also presents SAR images recovered from conventional interpolation techniques and the standard random projection based compressed sensing technique, both of which resulted in very poor SAR image quality at the same sub-Nyquist sampling rate (20%).

To move High-Performance Computing (HPC) closer to forward operating environments and missions, the Army Research Laboratory is developing approaches using hybrid, asymmetric core computing. By blending capabilities found in Graphics Processing Units (GPUs) and traditional von Neumann multicore Central Processing Units (CPUs), approaches are being developed and optimized to provide at or near real-time processing speeds for research project applications. Algorithms are designed to partition work to resources best designed to handle the processing load. The use of commodity resources allows the design to be flexible throughout the life cycle without the costly and time-consuming delays associated with Application-Specific Integrated Circuit (ASIC) development. This paradigm allows for rapid technology transfer to end users. In this paper, we describe a synchronous impulse reconstruction radar imaging algorithm that has been designed for hybrid CPU-GPU processing. We discuss various optimizations such as asynchronous task partitioning between the CPU and GPU as well as data movement reduction. We also discuss analysis and design of the algorithms within the context of two programming models: NVIDIA's CUDA and AMD's ATI Brook+. Finally, we report on the speedup achieved by this approach that allowed us to take a code once restricted to postprocessing and transform it into one that exceeds real-time performance requirements.

This paper presents a novel Sparsity-driven joint Image REgistration and Change Detection (SIRE-CD) technique for SAR imagery. The proposed algorithm simultaneously performs two main tasks: (i) locally register the test and reference images; and (ii) perform the change detection between the two. The key innovative concept here is the sparsity-driven transformation of the signatures from the reference image to match to those of the test image at the local image patch level. In other words, we are constructing a large dictionary from the reference data and use that to find the sparsest representation that best approximates the new incoming test data. The accuracy level of the approximation determines the detected changes between the reference and the test image. We demonstrate the performance of this technique using both simulated data and real SAR imagery from the Army Research Laboratory ultra-wideband (UWB) SAR forward-looking radar.

In this paper, we explore using the ultrawideband radar technology for sensing through-the-wall imaging. We conducted a field experiment with our in-house designed impulse-based radar, in which a stripmap synthetic aperture radar (SAR) geometry was employed in mapping an abandoned army barrack building with a large footprint area. The images obtained from two sides of the building were combined in order to obtain the full building layout, including the interior structure. Computer simulations performed with Xpatch, a physical optics-based ray tracing code, are used to explain some phenomena observed in the measured SAR images.

Three electromagnetic models are employed for the investigation of ultrawideband VHF scattering from tree trunks situated over flat and sloped terrain. Two of the models are numerical, each employing a frequency-domain integral-equation formulation solved via the method of moments (MoM). A body-of-revolution (BoR) Mote formulation is applied for a tree trunk on a flat terrain, implying that the BoR axis is perpendicular to the layers of an arbitrary layered-earth model. For the case of sloped terrain, the BoR model is inapplicable, and therefore the MoM solution is performed via general triangular-patch basis functions. Both MoM models are very accurate but are computationally expensive. Consequently, the authors also consider a third model, employing approximations based on the closed-form solution for scattering from an infinite dielectric cylinder in free space. The third model is highly efficient computationally and, despite the significant approximations, often yields accurate results relative to data computed via the reference MoM solutions. Data from the three models are considered, and several examples of application to remote sensing are addressed.

Radar scattering from an illuminated object is often highly dependent on the target-sensor orientation. In typical synthetic aperture radar (SAR) imagery, the information in the multi-aspect target signatures is diffused in the image-formation process. In an effort to exploit the aspect dependence of the target signature, the authors employ a sequence of directional filters to the SAR imagery, thereby generating a sequence of subaperture images that recover the directional dependence of the target scattering. The scattering statistics are then used to design a hidden Markov model (HMM), wherein the orientation-dependent scattering statistics are exploited explicitly. This approach fuses information embodied in the orientation-dependent target signature under the assumption that. Both the target identity and orientation are unknown. Performance is assessed by considering the detection of tactical targets concealed in foliage, using measured foliage-penetrating (FOPEN) SAR data.