Multiple target localization and power estimation in wireless sensor networks using compressive sensing

In this paper, we propose a multiple target localization and power estimation approach in wireless sensor networks using compressive sensing (CS). While this is not the first work on applying CS to localize targets, it is the first to achieve localization without the prior knowledge of transmitting powers of targets. The locations and transmitting powers of targets are formulated as a sparse vector in the discrete spatial domain and the received signal strengths (RSSs) are taken to reconstruct the sparse vector. Our approach consists of two stages: an offline stage and an online stage. At the offline stage, the sensing matrix is constructed by collecting RSSs from RF emitters, avoiding the disadvantage of radio propagation model. Then, at the online stage, a small number of RSS measurements are taken for exact recovery of the sparse vector. Finally, simulation results demonstrate the effectiveness and robustness of our localization and power estimation approach.