Distributed Filtering over Networks Using Greedy Gossip

This paper studies the problem of distributed filtering for state estimation of a dynamic system by using observations from sensors in a network, and proposes a greedy gossip-based distributed filtering (GG-DF) algorithm. The sensor nodes can make estimation and work collaboratively. The information transmission across the network abides by the asynchronous gossip strategy that only two neighboring nodes are selected to communicate and exchange information with each other in each communication round. First, we propose a cost function of the estimation error of the entire network. Then, we derive our algorithm by making a greedy selection to minimize the cost. Finally, we provide performance and convergence analysis of the proposed algorithm, along with simulation results compared with existing methods.