In this paper, a composite weighted average consensus filtering algorithm (CWACF) is proposed by applying multiple heterogeneous nonlinear filters. In light of the sensors’ different sensing accuracy and computational capability, extended Kalman filter (EKF) and sparse-grid quadrature filter (SGQF) are compositely adopted on different sensors as local filters. Then, estimation from neighbours are fused based on the weighted average consensus framework to attain better estimation performance. Moreover, it has been proved that the estimation error is exponentially bounded in mean square. The performance of the proposed algorithm and distributed extended Kalman filtering (DEKF) are compared by a target localization case through a sensor network.
Composite Weighted Average Consensus Filtering
01.08.2018
164177 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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