Multi-view indoor scene reconstruction from compressed through-wall radar measurements using a joint bayesian sparse representation

This paper addresses the problem of scene reconstruction, incorporating wall-clutter mitigation, for compressed multi-view through-the-wall radar imaging. We consider the problem where the scene is sensed using different reduced sets of frequencies at different antennas. A joint Bayesian sparse recovery framework is first employed to estimate the antenna signal coefficients simultaneously, by exploiting the sparsity and correlations between antenna signals. Following joint signal coefficient estimation, a subspace projection technique is applied to segregate the target coefficients from the wall contributions. Furthermore, a multitask linear model is developed to relate the target coefficients to the scene, and a composite scene image is reconstructed by a joint Bayesian sparse framework, taking into account the inter-view dependencies. Experimental results show that the proposed approach improves reconstruction accuracy and produces a composite scene image in which the targets are enhanced and the background clutter is attenuated.

1. Introduction

Through-the-wall radar imaging (TWRI) is emerging as a powerful technology for numerous civilian and military applications [1], [2]. In practice, TWRI faces several interferences, such as layover and shadow effects, which impede target detection and localization. For example, when the antenna is placed facing a strong reflective target with another weak target behind, layover effects occur, rendering the detection of the weak target more difficult, or impossible. Further, the target reflectivity depends highly on the sensing aspect angle. Target reflections may be strong if sensed from the front wall, but may be weak when illuminated from the side wall, and vice versa. These problems can be addressed by using multi-view or multitask-location sensing and then combining the data acquired from different vantage points to enhance image formation and target de-tection.