Radio-Frequency Tomographic Tracking of a Time-Varying Number of Targets with Wireless Sensor Networks

Abstract

Radio-frequency (RF) tomographic tracking is an emerging technology which tracks moving targets by analyzing changes of received signal strength (RSS) in wireless links. This paper presents and evaluates a novel RF tomographic tracking system that is capable of tracking a time-varying number of targets in wireless sensor networks (WSNs). The system incorporates two major contributions: a RSS histogram based observation model and a multi-target filtering algorithm based on multi-Bernoulli approximation. In addition, the sequential Monte Carlo method is applied to implement the multi-target filter. To evaluate the tracking system, an experiment involving 3 targets is performed within an indoor area of 50 square meters. Experimental results demonstrate that the proposed tracking system achieves high performance in accuracy and efficiency.