Radio tomographic imaging based on cluster Bayesian compressive sensing

Abstract

In narrowband radio tomographic imaging, the crucial challenge is to detect multipath interference effectively and obtain a better estimate of shallow fading. Based on structural cluster Bayesian compressive-sensing theory, an analysis is presented of the possible shallow fading status, and more spatial distribution information of the shallow fading is explored. As a result, a more accurate prior model combining the sparsity and cluster property of shallow fading and a better computational imaging mechanism is proposed. The experimental results show that this cluster sparsity Bayesian compressive-sensing model restrains the artifacts in the image by improving the link discrimination ability between shallow fading and multipath interference so that better recovered images can be obtained, and device-free localization performance is improved.