| Citation: | LIU Shuai, ZHAO Guorong, ZENG Bin, et al. Distributed moving horizon estimation under constraints of quantized measurements and packet dropouts[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(8): 1485-1493. doi: 10.13700/j.bh.1001-5965.2019.0497(in Chinese)
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Aimed at the problem of network constraint, distributed state estimation for networked systems with packet dropouts and quantized measurements is studied. A group of Bernoulli distributed random variables is employed to describe the phenomenon of packet dropouts, and a prediction compensation mechanism is applied to compensate the packet dropouts. Quantized errors introduced by data quantification are described as parameter uncertainty in the observation equation, and the local estimator is obtained by solving a min-max problem in fixed time domain. The stability of the local estimator is studied, and a sufficient condition for the convergence of the expectation of the square norm of estimation error is obtained. For each local estimator, the recursive formula of the upper bound of the error covariance is derived, based on which a distributed fusion estimator is presented by using the Covariance Intersection (CI) fusion algorithm. The simulation results show that the proposed algorithm can effectively reduce the influence of packet dropouts and quantization on state estimation.
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