Distributed Multitarget Tracking Based on Diffusion Strategies over Sensor Networks

We consider the distributed multitarget tracking over sensor networks, where each node only communicates with its neighbors. We develop a diffusion-based distributed multisensor multitarget tracking algorithm. The state update of the diffusion-based distributed algorithm is mainly composed of two phases: an adaptation phase and a combination phase. During the adaptation phase, each node updates its local estimate by using all its neighbors' measurements. It is achieved based on a multi-sensor cardinalized probability hypothesis density filter. During the combination phase, each node fuses all its neighbors' local estimates. It is achieved based on a generalized version of covariance intersection technique. Compared to the consensus-based distributed algorithm, the proposed algorithm has two advantages. First, it can provide more accurate and robust tracking results, especially when the detection probability that the sensors detect the targets is low. Second, it has lower communication load because the consensus iterations are not required. Numerical results are provided to illustrate the performance of the proposed algorithm.