Ground Target Tracking with Acoustic Sensors using Particle Filters and Statistical Data Association

In this paper the tracking of ground targets using acoustic sensors, distributed in a wireless sensor network, is studied. Since only acoustic sensors are utilized in the study the tracking problem can be regarded as a bearings-only application. The solution to the problem is given within the Bayesian recursive framework, where a sequential Monte Carlo method to the ground target tracking problem is developed. The classical sampling importance resampling (SIR) scheme is redesigned to also track multiple targets. The approach for solving the data association problem is based on hypothesis calculations according to the joint probabilistic data association (JPDA) method. Validation and evaluation of the tracking algorithms are performed using simulated data as well as real data extracted from a ground sensor network.