Toward Accurate Device-Free Wireless Localization With a Saddle Surface Model

Abstract

Device-free wireless localization (DFL) is a technique that can locate a target by analyzing its shadowing effect on wireless links, which causes the variation of link measurements, while removing the requirement of equipping the target with a device. It can provide fundamental data for pervasive computing, smart environment, and traffic surveillance applications. The observation model, which represents the relationship between wireless link measurement and target location, is very important for DFL, since it characterizes the shadowing effect of the target on wireless links and, therefore, determines the performance of the DFL system. In this paper, inspired by measurement results, we propose a saddle surface (SaS) model to describe the shadowing effect. The SaS model characterizes the elaborate information within the spatial impact area and provides more useful observation information for the location estimation algorithm. We incorporate the SaS model into the particle filter framework for location estimation. Extensive experiments in indoor and outdoor scenarios are carried out to evaluate the performance of the proposed schemes. The tracking errors of 0.78 and 0.21 m in the given two scenarios demonstrate the better performance of the proposed SaS model compared with existing models.