Distributed tracking with constrained communication

In realistic scenarios, distributed tracking is hindered due to limitations on the communication capacity between sensor nodes. In this paper, solutions for measurement fusion and track-to-track fusion in communication constrained distributed sensor networks are summarized. A comparison of the communication required and the target state estimate error of a central and distributed Kaiman filter is presented. It is shown that the distributed Kaiman filter requires significantly less communication than the central Kaiman filter for realistic false measurement densities, and consequently the distributed Kaiman filter achieves a significantly lower estimation error given a communication constraint.