Adaptive Distributed State and Input Estimation Using Retrospective-Cost-Based Information Filter

In this paper, the problem of distributed state and input estimation using sensor networks for linear system is investigated. First, the retrospective-cost-based information filter (RCIF) is proposed to estimate the state and input simultaneously, by combining the retrospective cost input estimator subsystem and information filter state estimator subsystem. Next, the retrospective-cost adaptive input estimator subsystem is formulated, which utilizes retrospective cost optimization and recursive minimum mean square estimation to drive the estimated input to approximate the actual input without prior information. Then, the consensus algorithm is used to extend the RCIF to distributed estimation, and to improve the convergence rate, the adaptive update law of consensus weights is presented. Finally, a simulation example is illustrated to validate the effectiveness and feasibility of the proposed algorithm.