Robust multi-target tracking in RF tomographic network

Abstract

Radio tomographic imaging (RTI) is a promising technique which allows localizing and tracking targets carrying no electronic devices. It utilizes the attenuation of wireless links to generate images of the change in the propagation field. Objects that obstruct the wireless signals in the field will lead to bright blobs in RTI image. For multi-target tracking, we employ clustering to obtain cluster observations to assign to targets. However, the blob corresponding to a target may be divided into several clusters in the process of clustering. The phenomenon is called over-clustering, i.e., there will be several cluster observations originated from the same target. Global nearest neighbor (GNN) which is popular in data association is optimal only under the assumption that only one cluster is originated from a target. However over-clustering will reduce the multi-target tracking performance of GNN. In this paper, the joint probabilistic data association (JPDA) method which is robust to over-clustering is proposed to improve the multi-target tracking performance when over-clustering is present. Real experiments are conducted in a monitored region surrounded by 20 RF sensors. When over-clustering is present, the experimental results show that the minimum tracking error of JPDA and GNN is 0.24m and 0.37m, respectively.