Compressive sensing for through-the-wall radar imaging

Abstract

Through-the-wall radar imaging (TWRI) is emerging as a viable technology for providing high-quality imagery of enclosed structures. TWRI makes use of electromagnetic waves to penetrate through building wall materials. Due to the "see" through ability, TWRI has attracted much attention in the last decade and has found a variety of important civilian and military applications. Signal processing algorithms have been devised to allow proper imaging and image recovery in the presence of high clutter, which is caused by front walls and multipath due to reflections from internal walls. Recently, research efforts have shifted toward effective and reliable imaging under constraints on aperture size, frequency, and acquisition time. In this respect, scene reconstructions are being pursued with reduced data volume and within the emerging compressive sensing (CS) framework. We present a review of the CS-based scene reconstruction techniques that address the unique challenges associated with fast and efficient imaging in urban operations. Specifically, we focus on ground-based imaging systems for indoor targets. We discuss CS-based wall mitigation, multipath exploitation, and change detection for imaging of stationary and moving targets inside enclosed structures. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

“…The benefits provided by sparsitydriven imaging are even greater in such non-conventional sensing scenarios. Sparsity-driven imaging has also been used for the problem of inverse SAR (ISAR) imaging of rotating targets [16], as well as for through-the-wall radar imaging [17]. It has also been extended to interferometric SAR [18] and SAR tomography (TomoSAR) [19] adding the elevation direction into the problem for 3-D imaging, as well as to 4-D (differential, i.e., spacetime)…”

Sparsity-Aware Human Motion Indication

“…In TWRI, this has the advantage of reducing the number of measurement samples and data acquisition and processing time. A number of CS-based methods were proposed for TWRI in recent years [5,8,23,24,30,31]. In this section, we review briefly the CS-based approach for solving the image formation problem as an inverse problem using the single measurement vector model.…”

“…Recently, CS has been considered for radar imaging due to its ability to reconstruct a high-resolution image from a reduced set of measurements [2,5,17,18,23,24,31]. The scene reconstruction is posed as an inverse problem, whereby a spatial map of reflections is formed from radar measurements.…”

Multi-target simultaneous ISAR imaging based on compressed sensing

“…It has been proposed in [12] that ISAR image can be reconstructed using much fewer pulses than RD algorithm with random pulse repetition interval (PRI). At present, most of the research on CS imaging is aimed at a single target and has achieved some research results [7][8][9][10]12]. Considering the requirement of radar multi-target observation and imaging, this paper tries to use the CS imaging method to improve the ability of simultaneous multitarget radar imaging.…”

“…It suggests that the signal can be sampled at sub-Nyquist rate and be reconstructed correctly if the signal is sparse or compressive in some basis or transform domain [5][6][7][8][9]. ISAR imaging based on CS is also an active research area since the targets often show sparse reflections and occupy only limited pixels in the imaging results [10,11]. It has been proposed in [12] that ISAR image can be reconstructed using much fewer pulses than RD algorithm with random pulse repetition interval (PRI).…”