Distributed information-weighted Kalman consensus filter for sensor networks

Abstract

Consensus-based algorithms for distributed Kalman filtering of the state of a dynamical target agent have attracted considerable research and attention during the past decade. In these filters, it is required for all agents to reach consensus about their estimates of the state of a target node. Distributed filtering techniques for sensor networks require less computation per sensor node and result in more robust estimation since they only use information from an agent’s neighbors in a network. However, poor local sensor node estimates caused by limited observability, network topologies that restrict allowable communications, and communication noises between sensors are challenging issues not yet fully resolved in the framework of distributed Kalman consensus filters. This paper confronts these issues by introducing a novel distributed information-weighted Kalman consensus filter (IKCF) algorithm for sensor networks in a continuous-time setting. It is formally proven using Lyapunov techniques that, using the new distributed IKCF, the estimates of all sensors reach converge to consensus values that give locally optimal estimates of the state of the target. A new measurement model is selected that only depends on local information available at each node based on the prescribed communication topology, wherein all the estimates of neighbor sensors are weighted by their inverse-covariance matrices. Locally optimal solutions are then derived for the proposed distributed IKCF considering channel noises in the consensus terms. Moreover, if the target has a nonzero control input, a method is giving of incorporating estimates of the target’s unknown input. Simulation case studies show that the proposed distributed IKCF outperforms other methods in the literature.