Device-Free Localization via Dictionary Learning With Difference of Convex Programming

In this paper, we consider a method to solve the device-free localization (DFL) problem that is able to detect spatial obstruction via wireless network. A dictionary learning approach with difference of convex (DC) programming and DC algorithm is proposed to indicate target location based on learning data. By measuring the variation in the received signal strength of the wireless links indicating the locations of the obstructions, the physical target in the monitoring area can be estimated through a learned dictionary. We show that the DFL problem can be efficiently formulated as a non-convex optimization problem. We adopt a penalty function called the minimax concave penalty, which possesses good properties in terms of seeking sparsity, and solve the non-convex optimization problem using DC programming. Furthermore, the localization accuracy achieved during the path-tracking task is further improved by the proposed tracking neighborhood rule. The rule provides a solution for increasing the localization accuracy under time-varying conditions generated by sampling channels of sensor networks under noisy conditions. The proposed approach is validated on a real-world dataset and has the potential to be adopted flexibly in DFL applications.