Optimal information based adaptive compressed radio tomographic imaging

This paper studies the adaptive sensing mechanism for compressed radio tomographic imaging (CRTI) with radio frequency (RF) sensor networks. Specifically, an optimal information approach is presented to design the projection measurements incrementally. In each step, based on the signal reconstruction from previous measurements using the sparse Bayesian learning (SBL) method, we exploit the mutual information maximization criterion to infer the optimal radio link for the next step. By benefiting from the feedback of the signal estimation, the approach presented can effectively find the correlated radio links to achieve adaptive sensing, and thus improve the sensing efficiency. Experimental results are reported to validate the proposed approach.