Distributed multi-target tracking using joint probabilistic data association and average consensus filter

In this paper, a distributed multi-target tracking (MTT) algorithm suitable for implementation in wireless sensor networks is proposed. For this purpose, the Monte Carlo (MC) implementation of joint probabilistic data-association filter (JPDAF) is applied to the well-known problem of multi-target tracking in a cluttered area. Also, to make the tracking algorithm scalable and usable for sensor networks of many nodes, the distributed expectation maximization algorithm is exploited via the average consensus filter, in order to diffuse the nodes’ information over the whole network. The proposed tracking system is robust and capable of modeling any state space with nonlinear and non-Gaussian models for target dynamics and measurement likelihood, since it uses the particle-filtering methods to extract samples from the desired distributions. To encounter the data-association problem that arises due to the unlabeled measurements in the presence of clutter, the well-known JPDAF algorithm is used. Furthermore, some simplifications and modifications are made to MC–JPDAF algorithm in order to reduce the computation complexity of the tracking system and make it suitable for low-energy sensor networks. Finally, the simulations of tracking tasks for a sample network are given.