Target tracking using proximity binary sensors
Qiang Le, Lance Kaplan · 2011
This paper investigates the feasibility of a mesh network of proximity sensors to track multiple targets. In such a network, the sensors report a detection when a target is within the proximity; otherwise, the sensors report no detection. Previous work has revealed the potential of target localization and tracking for a single target using these binary reports. This work introduces a particle-based probability hypothesis density (PHD) filter that is able to track multiple targets using the binary reports from a proximity sensor network. Furthermore, this work modifies another particle-based multitarget tracker for proximity sensors, namely the ClusterTrack, from 1-D tracking to 2-D. The simulations demonstrate that the PHD is able to outperform the Cluster- Track in terms of both accuracy of localization and estimating the number of targets.