Adaptive consensus‐based distributed state estimator for non‐linear systems in the presence of multiplicative noise

Abstract

The problem of consensus-based distributed state estimation of a non-linear dynamical system in the presence of multiplicative observation noise is investigated in this study. Generalised extended information filter (GEIF) is developed for non-linear state estimation in the information-space framework. To fuse the information contribution of local estimators, an average consensus algorithm is employed. To achieve faster convergence towards consensus, a novel technique is proposed to modify the consensus weights, adaptively. Computational complexity of the proposed estimator is also analysed theoretically to demonstrate the computational advantage of the adaptive consensus-based distributed GEIF over the centralised counterpart. Moreover, stability of local estimators in terms of mean-square boundedness of state estimation error is guaranteed, in the presence of multiplicative noise. Simulation results are provided to evaluate performance of the proposed adaptive distributed estimator for a target-tracking problem in a wireless sensor network.