Consensus-based distributed receding horizon estimation

This paper studies the distributed state estimation over sensor networks based on receding horizon estimation (RHE). Firstly, a new scheme of centralized RHE is introduced, which gathers the decomposition terms instead of collecting the measurements of each node. Then, we present a distributed estimate algorithm based on the centralized RHE. To avoid the quadratic programming (QP) problem, the proposed algorithm takes advantage of the analytical solution of the centralized RHE and performs consensus steps to generalize the distributed estimation for each node, which greatly reduces each node’s computation. Under the assumption of collective observability over networks, the proposed algorithm can guarantee the stability of estimation error in the case of enough consensus steps. Finally, the simulation results verify the effectiveness of the proposed method.