Data-efficient radio tomographic imaging with adaptive Bayesian compressive sensing

This work focuses on developing a data-efficient strategy for radio tomographic imaging with Bayesian compressive sensing. The task of our data-efficient strategy is to identify the informative yet non-redundant radio links in an adaptive fashion, which aims at reducing the fading uncertainties as well as the number of the received signal strength (RSS) measurements required. Our main contribution is to incorporate the fade-level of links into the informative optimization paradigm to form our adaptive link selection strategy. The advantage of our approach is to exclude the uninformative links with high fading uncertainties due to the multipath components, which contributes to the data efficiency yet imaging performance enhancement. Experimental results are reported to validate our approach.