Multiple Target Localization Using Compressive Sensing

In this paper, a novel multiple target localization approach is proposed by exploiting the compressive sensing theory, which indicates that sparse or compressible signals can be recovered from far fewer samples than that needed by the Nyquist sampling theorem. The authors formulate the multiple target locations as a sparse matrix in the discrete spatial domain. The proposed algorithm uses the Received Signal Strengths (RSSs) to find the location of targets. Instead of recording all RSSs over the spatial grid to construct a radio map from targets, far fewer numbers of RSS measurements are collected, and a data pre-processing procedure is introduced. Then