Abstract
In this paper, finite-time distributed state estimation problem for maneuvering target based on switching directed graph is investigated. Each sensor in the sensor network communicates only with its neighboring nodes and tries to maximize its observation for the target. Based on the information theory, the information form of the cubature Kalman filter is adopted and the distributed cubature information filtering (DCIF) algorithm is established by two phases, named local filter and consensus fusion. Considering the uncertainty of the environment and the target maneuvers, the topologies of the directed sensor network are designed to be switchable, and the switching topologies distributed cubature information filtering (ST-DCIF) algorithm is proposed. By means of the stochastic theory and Lyapunov method, the stability of the proposed ST-DCIF algorithm is analyzed. Meantime, the estimate error covariance matrix of the ST-DCIF algorithm is proved to converge to the results of the optimal centralized estimation algorithm when the sensor network is collective observable. In the final, the numerical simulation example for distributed estimation with switching topologies is given and the effectiveness of the proposed algorithm is validated.
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