Wall mitigation techniques for indoor sensing within the compressive sensing framework

Abstract

Compressive sensing (CS) for urban operations and through-the-wall radar imaging has been shown to be successful in fast data acquisition and moving target localizations. However, the research work in this area thus far has assumed prior effective wall removal, allowing proper detection of indoor targets. In this paper, we show that wall removal techniques, operating with full data volume and applying backprojection imaging methods, can be equally effective under reduced data volume and within the sparse signal reconstruction framework. Specifically, we demonstrate that the spatial filtering and the singular value decomposition based approaches, which, respectively, exploit the spatial invariance and the strength of the EM wall return, for suppression of the wall reflections, can be employed using few measurements, thus allowing CS to be applied to data with higher target-to-wallclutter ratio.