Consensus algorithm for distributed state estimation in multi-clusters sensor network

Abstract

Considering the convergence rate is a very important issue as distributed sensors networks usually consist of low-powered wireless devices and speeding up the consensus convergence rate is also important to reduce the number of messages exchanged among neighbors, a new adaptive method for weight assignment of communication links between sensor nodes is proposed based on the dynamic network topology. Based on the adaptive weight assignment method, an improved Kalman consensus filter (KCF) named IKCF is tailored in this letter for distributed state estimation in sensor networks with cluster structure. Furthermore, the experiments demonstrate the adaptive weight assignment method is effective for distributed state estimation when the sensor network is sparsely deployed. In addition, the simulation results also validate the superior performance of the new algorithm and show that IKCF is an excellent algorithm for multi-clusters sensor networks. And there is no additional communication overhead in IKCF because only some local knowledge is used to autonomously calculate the adaptive consensus rate parameter for each node.