Ellipsoid-density-based set-membership global estimation for complex networked systems with absolute and relative measurements

Abstract

This paper investigates the global state estimation problem under the influence of unknown-but-bounded process and measurement noises in complex networks. To improve state estimation accuracy and reliability, this paper first designs a distributed set-membership estimation scheme based on absolute and relative measurements. This scheme aims to obtain local state estimation ellipsoidal sets for the considered network nodes subject to unknown-but-bounded noises, which utilizes the relative measurement information among sensor nodes to enhance the accuracy of these local estimations. In addition, considering the accuracy requirements, an ellipsoid-density-based fusion method is proposed to fuse all the local estimation ellipsoids into a global state estimation ellipsoid, thereby improving the accuracy and confidence of the global state estimation. Finally, the effectiveness of the proposed method is verified by simulation examples, demonstrating its superiority for the global state estimation problem.