Multi-target Tracking in Wireless Sensor Networks Using Distributed Joint Probabilistic Data Association and Average Consensus Filter

The aim of this paper is to develop a distributed multi-target tracking (MTT) algorithm over wireless sensor networks which has the ability of online implementation in low-cost sensor nodes due to its lower computational complexity and execution time compared to the other MTT systems. The Monte Carlo (MC) implementation of JPDAF (MC-JPDAF) is applied to the classical problem of TT in a cluttered area. Also, to make the tracking algorithm scalable and usable for large networks, the distributed Expectation Maximization (EM) algorithm is used via the average consensus filter in order to diffuse the nodespsila information over the whole network. 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 the proposed system are given.