Distributed State Estimation for Discrete-Time Nonlinear System with Unknown Inputs

This paper investigated the problem of distributed estimation for a class of discrete-time nonlinear systems with unknown inputs in a sensor network. A modification scheme to the derivative-free versions of nonlinear robust two-stage Kalman filter (DNRTSKF) is first introduced based on recently developed cubature Kalman filter (CKF) technique. Afterward, a novel information filter is proposed by expressing the recursion in terms of the information matrix based upon DNRTSKF. In the end, distributed DNRTSKF is developed by applying a new information consensus filter to diffuse local statistics over the entire sensor network. In the implementation procedure, each sensor node only fuses the local observation instead of the global information and updates its local information state and matrix from its neighbors’ estimates using Average-Consensus Algorithm. Simulation results illustrate that the proposed distributed filter reveals the performance comparable to centralized DNRTSKF and better than distributed CKF.