Abstract: Since the localization problem in wireless sensor networks has an intrinsic sparse nature,the compressive sensing (CS) theory has been wildly used to achieve target localization with limited number of measurements.However,most of the existing CS-based localization approaches require the prior knowledge of transmitting powers of targets,which is not conformed to the reality that targets are complete unknown.Thus,we proposed a multiple target localization and power estimation approach,which formulates the locations and transmitting powers of targets as a sparse vector,transforming the localization and power estimation problem into a sparse vector estimation problem.Our work includes two stages:the offline stage and online stage.The main task of offline stage is deploying some RF emitters and collecting the received signal strength (RSS) to construct the sensing matrix.At the online stage,by deploying a small number of sensors to measure RSSs from targets and solving the ₁-minimization program,the sparse vector can be accurately recovered.Finally,simulation results demonstrate the effectiveness and robustness of our localization and power estimation approach.

Key words: Multiple target localization,Transmitting power estimation,Compressive sensing,Wireless sensor networks