期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 51, 期 3, 页码 1892-1903出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2019.2907649
关键词
Censored measurements; distributed filtering; H-infinity-consensus; multiplicative noises; partial-nodes-based (PNB) measurements
资金
- National Natural Science Foundation of China [61873148, 61873058]
- China Post-Doctoral Science Foundation [2017M621242]
- PetroChina Innovation Foundation [2018D-5007-0302]
- Fundamental Research Funds for Undergraduate Universities affiliated to Heilongjiang Province [2018QNL-05]
- Natural Science Foundation of Heilongjiang Province of China [F2018005]
- Alexander von Humboldt Foundation of Germany
This paper addresses the scalable distributed H-infinity-consensus filtering problem for discrete time-varying systems subject to multiplicative noises and censored measurements over sensor networks. It introduces a new H-infinity-consensus performance index and establishes sufficient conditions for achieving the desired performance index using certain techniques.
This paper deals with the scalable distributed H-infinity-consensus filtering problem for a class of discrete timevarying systems subject to multiplicative noises and censored measurements over sensor networks (SNs). For the underlying SN, it is assumed that only the measurement outputs from partial sensor nodes are available. Also, the phenomenon of censored measurements is taken into account to reflect the limited capability in measuring. A new H-infinity-consensus performance index is put forward to evaluate the disturbance rejection level of the filters against the simultaneous presence of external disturbances, initial conditions, as well as censoring effects. By utilizing the vector dissipativity theory and the recursive matrix inequality technique, sufficient conditions are established under which the prescribed H-infinity-consensus performance index is achieved. The parameters of the desired distributed filters are calculated via solving certain matrix inequalities, where such a calculation is conducted in a local sense so as to preserve the scalability of the filter design. Finally, a numerical simulation example is provided to demonstrate the validity and applicability of the proposed filtering strategy.
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