A Multiple Target Localization with Sparse Information in Wireless Sensor Networks

Abstract

It is a great challenge for wireless sensor network to provide enough information for targets localization due to the limits on application environment and its nature, such as energy, communication, and sensing precision. In this paper, a multiple targets localization algorithm with sparse information (MTLSI) was proposed using compressive sensing theory, which can provide targets position with incomplete or sparse localization information. It does not depend on extra hardware measurements. Only targets number detected by sensors is needed in the algorithm. The monitoring region was divided into a plurality of small grids. Sensors and targets are randomly dropped in grids. Targets position information is defined as a sparse vector; the number of targets detected by sensor nodes is expressed as the product of measurement matrix, sparse matrix, and sparse vector in compressive sensing theory. Targets are localized with the sparse signal reconstruction. In order to investigate MTLSI performance, BP and OMP are applied to recover targets localization. Simulation results show that MTLSI can provide satisfied targets localization in wireless sensor networks application with less data bits transmission compared to multiple targets localization using compressive sensing based on received signal strengths (MTLCS-RSS), which has the same computation complexity as MTLIS.