Exploiting fine-grained subcarrier information for device-free localization in wireless sensor networks

Abstract

Device-free localization (DFL) that aims to localize targets without carrying any electronic devices is addressed as an emerging and promising research topic. DFL techniques estimate the locations of transceiver-free targets by analyzing their shadowing effects on the radio signals that travel through the area of interest. Recently, compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements by exploiting the inherent spatial sparsity of target locations. In this paper, we propose a novel CS-based multi-target DFL method to leverage the frequency diversity of fine-grained subcarrier information. Specifically, we build the dictionaries of multiple channels based on the saddle surface model and formulate the multi-target DFL as a joint sparse recovery problem. To estimate the location vector, an iterative location vector estimation algorithm is developed under the multitask Bayesian compressive sensing (MBCS) framework. Compared with the state-of-the-art CS-based multi-target DFL approaches, simulation results validate the superiority of the proposed algorithm.