Moving-Horizon Estimation for Linear Dynamic Networks With Binary Encoding Schemes

This article is concerned with moving-horizon state estimation problems for a class of discrete-time linear dynamic networks. The signals are transmitted via noisy network channels and distortions can be caused by channel noises. As such, the binary encoding schemes, which take advantages of the rob...

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Veröffentlicht in:IEEE transactions on automatic control Jg. 66; H. 4; S. 1763 - 1770
Hauptverfasser: Liu, Qinyuan, Wang, Zidong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Binary data
Binary encoding schemes
Channels
communication constraints
Crossovers
Distortion
dynamic networks
Encoding
Estimators
Finite wordlength effects
Kalman filtering
Kalman filters
Moving horizon state estimation
moving-horizon estimation
Optimization
remote state estimation
Robustness
Signal transmission
State estimation
ISSN:0018-9286, 1558-2523
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Zusammenfassung:This article is concerned with moving-horizon state estimation problems for a class of discrete-time linear dynamic networks. The signals are transmitted via noisy network channels and distortions can be caused by channel noises. As such, the binary encoding schemes, which take advantages of the robustness of the binary data, are exploited during the signal transmission. More specifically, under such schemes, the original signals are encoded into a bit string, transmitted via memoryless binary symmetric channels with certain crossover probabilities, and eventually restored by a decoder at the receiver. Novel centralized and decentralized moving-horizon estimators in the presence of the binary encoding schemes are constructed by solving the respective global and local least-square optimization problems. Sufficient conditions are obtained through intensive stochastic analysis to guarantee the stochastically ultimate boundedness of the estimation errors. A simulation example is presented to verify the effectiveness of the proposed moving-horizon estimators.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2020.2996579