Average Consensus Based Scalable Robust Filtering for Sensor Network

The distributed or scalable robust filtering problem using average consensus strategy in a sensor network is investigated in this paper. Specifically, based on the information form robust filter, every node estimates the global average information contribution using local and neighbors´ information rather than using the information from whole network. Due to the adoption of iterations of robust filter, the proposed algorithm relaxes the necessity to have the prior knowledge of the noise statistics. Moreover, the proposed algorithm is applicable to large-scale sensor network since each node broadcasts message only to its neighboring nodes. A numerical example on the application of the proposed algorithm to track a target moving on noisy circular trajectories is given.