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.
Highlights
Location information is an important ingredient for many location-based services (LBS) [1].With the increasing demand of LBS, target localization technique has attracted considerable attention.In the last decade, extensive research works on target localization have been carried out by the scientific community
To exploit the frequency diversity of the channel state information (CSI) measurements collected from multiple channels, we model the device-free localization (DFL) problem as a joint sparse recovery problem and develop an iterative location vector estimation algorithm to solve this problem under the multitask Bayesian compressive sensing (MBCS) framework
In order to combat the negative effect of measurement noises and achieve better counting and localization performance, we propose to exploit the frequency diversity of the measurements collected from multiple channels and realize joint sparse recovery in DFL
Summary
Location information is an important ingredient for many location-based services (LBS) [1]. LCS [20] is a representative work, which estimates the sparse location vector based on the greedy matching pursuit (GMP) algorithm and has proven that the product of dictionary obeys RIP with high probability As another CS-based DFL approach, E-HIPA [8]. The multi-target DFL is formulated as a joint sparse recovery problem To solve this problem, an iterative location vector estimation algorithm is developed under the multitask Bayesian compressive sensing (MBCS) [29]. Different from previous CS-based DFL methods, the proposed method leverages the frequency diversity of fine-grained subcarrier information to improve the localization accuracy. We formulate the multi-target DFL as a joint sparse recovery problem, and develop a novel location vector estimation algorithm to solve this problem under the MBCS framework.
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