Through-the-wall radar imaging with compressive sensing; theory, practice and future trends-a review

Through-the-Wall Radar Imaging (TWRI) is anemerging technology which enables us to detect behind the wall targets using electromagnetic signals. TWRI has received considerable attention recently due to its diverse applications. This paper presents fundamentals, mathematical foundations and emerging applications of TWRI with special emphasis on Compressive Sensing (CS) and sparse image reconstruction.Multipath propagation stemming from the surrounding walls and nearby targets are among the impinging challenges.Multipath components produce replicas of the genuine target, ghosts, during image reconstruction which may significantly increase the probability of false alarm. The resulting ghost not only creates confusion with genuine targets but may deteriorate the performance of (CS) algorithms as described in this article. The results from a practical scenario show a promising future of the technology which can be adopted in real-life problems including rescue missions and military purposes.AKey words: spect dependence, compressive sensing, multipath ghost, multipath exploitation, through-the-wall-radar imaging

Performance Evaluation of Aspect Dependent-Based Ghost Suppression Methods for Through-the-Wall Radar Imaging

There are many approaches which address multipath ghost challenges in Through-the-Wall Radar Imaging (TWRI) under Compressive Sensing (CS) framework. One of the approaches, which exploits ghosts’ locations in the images, termed as Aspect Dependent (AD), does not require prior knowledge of the reflecting geometry making it superior over multipath exploitation based approaches. However, which method is superior within the AD based category is still unknown. Therefore, their performance comparison becomes inevitable, and hence this paper presents their performance evaluation in view of target reconstruction. At first, the methods were grouped based on how the subarrays were applied: multiple subarray, hybrid subarray and sparse array. The methods were fairly evaluated on varying noise level, data volume and the number of targets in the scene. Simulation results show that, when applied in a noisy environment, the hybrid subarray-based approaches were robust than the multiple subarray and sparse array. At 15 dB signal-to-noise ratio, the hybrid subarray exhibited signal to clutter ratio of 3.9 dB and 4.5 dB above the multiple subarray and sparse array, respectively. When high data volumes or in the case of multiple targets, multiple subarrays with duo subarrays became the best candidates. Keywords: Aspect dependent; compressive sensing; point target; through-wall-radar imaging

Target-to-Target Interaction in Through-the-Wall Radars under Path Loss Compensated Multipath Exploitation-Based Signal Model for Sparse Image Reconstruction

Multipath caused by reflections from interior walls of buildings has been a long-standing challenge that affects Through-the-Wall Radar Imaging. Multipath creates ghost images that introduce confusion when detecting desired targets. Traditionally, multipath exploitation techniques under the compressive sensing framework have widely been applied to address the challenge. However, the multipath component emanating from target-to-target interactions has not been considered–a consequence that may, under multiple target scenarios, lead to incorrect image interpretation. Besides, far targets experience more attenuation due to free space path-loss, hence resulting into target undetectability. This study proposes a signal model, based on multipath exploitation techniques, by designing a sensing matrix that incorporates multipath returns due to target-to-target interaction and path loss compensation. The study, in addition, proposes the path-loss compensator that, if integrated into the proposed signal model, reduces path loss effects. Simulation results show that the Signal to Clutter Ratio and Relative Clutter Peak improved by 4.9 dB and 1.9 dB, respectively compared to the existing model.Keywords: Compressive sensing, multipath ghost, multipath exploitation, pathloss, path-loss compensator, through-the-wall-radar imaging

Behind-wall target detection using micro-doppler effects

Abstract: During the last decade technology for seeing through walls and through dense vegetation has interested many researchers. This technology offers excellent opportunities for military and police applications, though applications are not limited to the military and police; they go beyond those applications to where detecting a target behind an obstacle is needed. To be able to disclose the location and velocity of obscured targets, scientists’ resort to electromagnetic wave propagation. Thus, through-the-wall radar (TWR) is technology used to propagate electromagnetic waves towards a target through a wall. Though TWR is a promising technology, it has been reported that TWR imaging (TWRI) poses a range of ambiguities in target characterisation and detection. These ambiguities are related to the thickness and electric properties of walls. It has been reported that the mechanical and electric properties of the wall defocus the target image rendered by the radar. The defocusing problem is the phenomenon of displacing the target away from its true location when the image is rendered. Thus, the operator of the TWR will have a wrong position, not the real position of the target. Defocusing is not the only problem observed while the signal is travelling through the wall. Target classification, wall modelling and others are areas that need investigation...D.Ing. (Electrical and Electronic Engineering

Through-Wall Detection with LS-SVM under Unknown Wall Characteristics

One of the main challenges in through-wall imaging (TWI) is the presence of the walls, whose returns tend to obscure the target behind the walls and must be considered and computed in the imaging procedure. In this paper, a two-step procedure for the through-wall detection is proposed. Firstly, an effective clutter mitigation method based on singular value decomposition (SVD) is used. It does not require knowledge of the background scene or rely on accurate modeling and estimation of wall parameters. Then, TWI problem is cast as a regression one and solved by means of least-squares support vector machine (LS-SVM). The complex scattering process due to the presence of the walls is automatically included in the nonlinear relationship between the feature vector extracted from the target scattered fields and the position of the target. The relationship is obtained through a training phase using LS-SVM. Simulated results show that the proposed approach is effective. We also analyze the impacts of training samples and signalto-noise ratio (SNR) on test detection accuracy. Simulated results reveal that the proposed LS-SVM based approach can provide comparative performances in terms of accuracy, convergence, robustness, and generalization in comparison with the support vector machine (SVM) based approach