Set-Membership Filtering-Based Cooperative State Estimation for Multi-Agent Systems

In this article, we focus on the cooperative state estimation problem of multi-agent systems with absolute and relative measurements. The purpose of each agent is to estiamte its own state in a Set-Membership Filtering (SMFing) manner by utilizing locally available measurements and interchanging the necessary information among its neighbors. To handle this problem, we provide a centralized SMFing framework as a benchmark of distributed SMFs and propose a finite-horizon method called OIT-Inspired centralized constrained zonotopic algorithm. Moreover, we put forward a distributed SMFing framework and develop a distributed constrained zonotopic algorithm. Simulation results corroborate the effectiveness of our proposed algorithms.