Accurate and robust device-free localization approach via sparse representation in presence of noise and outliers

Device-free localization (DFL) aims at locating the positions of targets without carrying any emitting devices by monitoring the received signals of preset wireless devices. Research showed that the localization accuracy of conventional DFL algorithms decreases in presence of noise and outliers. To tackle this problem, this paper firstly proposes to study the DFL via sparse representation and the target localization is formulated as a sparse representation classification (SRC) problem. Specifically, an overcomplete sample dictionary is constructed by received signal strength and the target can be located by SRC method. To suppress the adverse impact of noise and outliers, we formulate the DFL-SRC problem in signal subspace. Two DFL algorithms termed as SDSRC and SSDSRC are derived. Experimental results with real recorded data and simulated interferences demonstrate that SDSRC and SSDSRC outperform the nonlinear optimization approach with outlier link rejection in terms of localization accuracy and robustness to noise and outliers.