期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 50, 期 5, 页码 1932-1942出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2018.2792009
关键词
Estimation; Markov processes; Quantization (signal); Sensors; Protocols; Linear systems; Communication channels; Markov jump systems; networked systems; optimal estimation; Riccati equations
资金
- China National Funds for Distinguished Young Scientists [61425009]
- National Natural Science Foundation of China [U1611262/61773357]
- Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar approved in 2015
- China National 863 Technology Projects [2015BAF32B03-05]
- Fundamental Research Funds for the Central Universities [2017FZA5010]
This paper focuses on the linear minimum mean square estimator for a networked discrete time-varying linear system subject to data quantification and communication constraints. The communication limitation is that only one transmission node can get access to the shared communication channel at each time step, and that different transmission nodes in the networked systems are scheduled to transmit information according to a Markov protocol. Then the remote estimator completes the estimation with only partially available observations, which are quantified. Suppose that the Markov chain is unknown to the remote estimator. By using orthogonal projection principle and innovation analysis method, a Kalman type filter is designed in a recurrence form. It is shown that estimation performance depends on the transition probability matrix of the Markov chain, quantization error, and the shared channel weighting parameter. Finally, an illustrative example is given to show the effectiveness of the proposed method.
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