Joint space aspect reconstruction of wide-angle SAR exploiting sparsity

In this paper we present an algorithm for wide-angle synthetic aperture radar (SAR) image formation. Reconstruction of wide-angle SAR holds a promise of higher resolution and better information about a scene, but it also poses a number of challenges when compared to the traditional narrow-angle SAR. Most prominently, the isotropic point scattering model is no longer valid. We present an algorithm capable of producing high resolution reflectivity maps in both space and aspect, thus accounting for the anisotropic scattering behavior of targets. We pose the problem as a non-parametric three-dimensional inversion problem, with two constraints: magnitudes of the backscattered power are highly correlated across closely spaced look angles and the backscattered power originates from a small set of point scatterers. This approach considers jointly all scatterers in the scene across all azimuths, and exploits the sparsity of the underlying scattering field. We implement the algorithm and present reconstruction results on realistic data obtained from the XPatch Backhoe dataset

Mitigation of Through-Wall Distortions of Frontal Radar Images using Denoising Autoencoders

Radar images of humans and other concealed objects are considerably distorted by attenuation, refraction and multipath clutter in indoor through-wall environments. While several methods have been proposed for removing target independent static and dynamic clutter, there still remain considerable challenges in mitigating target dependent clutter especially when the knowledge of the exact propagation characteristics or analytical framework is unavailable. In this work we focus on mitigating wall effects using a machine learning based solution -- denoising autoencoders -- that does not require prior information of the wall parameters or room geometry. Instead, the method relies on the availability of a large volume of training radar images gathered in through-wall conditions and the corresponding clean images captured in line-of-sight conditions. During the training phase, the autoencoder learns how to denoise the corrupted through-wall images in order to resemble the free space images. We have validated the performance of the proposed solution for both static and dynamic human subjects. The frontal radar images of static targets are obtained by processing wideband planar array measurement data with two-dimensional array and range processing. The frontal radar images of dynamic targets are simulated using narrowband planar array data processed with two-dimensional array and Doppler processing. In both simulation and measurement processes, we incorporate considerable diversity in the target and propagation conditions. Our experimental results, from both simulation and measurement data, show that the denoised images are considerably more similar to the free-space images when compared to the original through-wall images

Performance of Scattering Matrix Decomposition and Color Spaces for Synthetic Aperture Radar Imagery

Polarimetrc Synthetic Aperture Radar (SAR) has been shown to be a powerful tool in remote sensing because uses up to four simultaneous measurements giving additional degrees of freedom for processing. Typically, polarization decomposition techniques are applied to the polarization-dependent data to form colorful imagery that is easy for operators systems to interpret. Yet, the presumption is that the SAR system operates with maximum bandwidth which requires extensive processing for near- or real-time application. In this research, color space selection is investigated when processing sparse polarimetric SAR data as in the case of the publicly available \Gotcha Volumetric SAR Data Set, Version 1:0 . To improve information quality in resultant color imagery, three scattering matrix decompositions were investigated (linear, Pauli and Krogager) using two common color spaces (RGB, CMY) to determine the best combination for accurate feature extraction. A mathematical model is presented for each decomposition technique and color space to the Cramer-Rao lower bound (CRLB) and quantify the performance bounds from an estimation perspective for given SAR system and processing parameters. After a deep literature review in color science, the mathematical model for color spaces was not able to be computed together with the mathematical model for decomposition techniques. The color spaces used for this research were functions of variables that are out of the scope of electrical engineering research and include factors such as the way humans sense color, environment influences in the color stimulus and device technical characteristics used to display the SAR image. Hence, SAR imagery was computed for specific combinations of decomposition technique and color space and allow the reader to gain an abstract view of the performance differences

Sparse signal representation for complex-valued imaging

We propose a sparse signal representation-based method for complex-valued imaging. Many coherent imaging systems such as synthetic aperture radar (SAR) have an inherent random phase, complex-valued nature. On the other hand sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. For complex-valued problems, the key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. We propose a mathematical framework and an associated optimization algorithm for a sparse signal representation-based imaging method that can deal with these issues. Simulation results show that this method offers improved results compared to existing powerful imaging techniques

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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New Approach for Unambiguous High-Resolution Wide-Swath SAR Imaging

The high-resolution wide-swath (HRWS) SAR system uses a small antenna for transmitting waveform and multiple antennas both in elevation and azimuth for receiving echoes. It has the potential to achieve wide spatial coverage and fine azimuth resolution, while it suffers from elevation pattern loss caused by the presence of topographic height and impaired azimuth resolution caused by nonuniform sampling. A new approach for HRWS SAR imaging based on compressed sensing (CS) is introduced. The data after range compression of multiple elevation apertures are used to estimate direction of arrival (DOA) of targets via CS, and the adaptive digital beamforming in elevation is achieved accordingly, which avoids the pattern loss of scan-on-receive (SCORE) algorithm when topographic height exists. The effective phase centers of the system are nonuniformly distributed when displaced phase center antenna (DPCA) technology is adopted, which causes Doppler ambiguities under traditional SAR imaging algorithms. Azimuth reconstruction based on CS can resolve this problem via precisely modeling the nonuniform sampling. Validation with simulations and experiment in an anechoic chamber are presented

Salient Feature Identification and Analysis using Kernel-Based Classification Techniques for Synthetic Aperture Radar Automatic Target Recognition

An investigation into feature saliency for application to synthetic aperture radar (SAR) automatic target recognition (ATR) is presented. Specifically, research is focused on improving the SAR binary classification performance aspect of ATR, or the ability to accurately determine the class of a SAR target. The key to improving ATR classification performance lies in characterizing the salient target features. Salient features may be loosely defined as the most consistently impactful parts of a SAR target contributing to effective SAR ATR classification. To better understand the notion of salience, an investigation is conducted into the nature of saliency as applied to Air Force Research Lab\u27s (AFRL) civilian vehicle (CV) data domes simulated phase history data set. After separating vehicles into two SAR data classes, sedan and SUV, frequency and polarization features are extracted from SAR data and formed into either 1D high range resolution (HRR) or 2D spectrum parted linked image test (SPLIT) feature vectors. A series of experiments comparing vehicle classes are designed and conducted to focus specifically on the saliency effects of various SAR collection parameters including azimuth angle, aperture size, elevation angle, and bandwidth. The popular kernel-based Bayesian Relevance Vector Machine (RVM) classifier is utilized for sparse identification of relevant vectors contributing most to the creation of a hyperplane decision boundary. Analysis of experimental results ultimately leads to recommendations of the salient feature vectors and SAR collection parameters which provide the most potential impact to improving vehicle classification. Demonstrating the proposed saliency characterization algorithm with simulated civilian vehicle data provides a road map for salient feature identification and analysis of other SAR data classes in future operational scenarios. ATR practitioners may use saliency results to focus more attention on the identified salient features of a target class, improving efficiency and effectiveness of SAR ATR