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• W2000646173 abstract "In this paper, the problem of distributed weighted robust Kalman filter fusion is studied for a class of uncertain systems with autocorrelated and cross-correlated noises. The system under consideration is subject to stochastic uncertainties or multiplicative noises. The process noise is assumed to be one-step autocorrelated. For each subsystem, the measurement noise is one-step autocorrelated, and the process noise and the measurement noise are two-step cross-correlated. An optimal robust Kalman-type recursive filter is first designed for each subsystem. Then, based on the newly obtained optimal robust Kalman-type recursive filter, a distributed weighted robust Kalman filter fusion algorithm is derived for uncertain systems with multiple sensors. The distributed fusion algorithm involves a recursive computation of the filtering error cross-covariance matrix between any two subsystems. Compared with the centralized Kalman filter, the distributed weighted robust Kalman filter developed in this paper has stronger fault-tolerance ability. Simulation results are provided to demonstrate the effectiveness of the proposed approaches." @default.
• W2000646173 created "2016-06-24" @default.
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• W2000646173 date "2013-01-01" @default.
• W2000646173 modified "2023-10-02" @default.
• W2000646173 title "Distributed weighted robust Kalman filter fusion for uncertain systems with autocorrelated and cross-correlated noises" @default.
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• W2000646173 doi "https://doi.org/10.1016/j.inffus.2011.09.004" @default.
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