Distributed filtering based on randomized gossip strategy

This paper formulates and studies the problem of distributed filtering based on randomized gossip strategy in order to estimate the state of a dynamic system via all sensors in a network. First we introduce the randomized gossip algorithm by which the fastest averaging strategy can be obtained for a network with an arbitrary topology. Then we combine the randomized gossip algorithm with the information filter to design a randomized gossip based distributed filtering algorithm. The proposed method can adopt different communication volume flexibly, which results in different estimation performance. This flexibility distinguishes our method from the existing ones. Simulation examples verify that our method outperforms the diffusion strategy based distributed filtering algorithm if a small increase of communication requirements is allowed.