Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 52, Issue 11, Pages 12071-12083Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.3043283
Keywords
Estimation; Delays; State estimation; Fading channels; Digital TV; Mathematical model; Couplings; Communication delays; delay compensation-based estimation; dynamical bias; incomplete observations; monotonicity analysis; time-varying stochastic complex networks (CN)
Categories
Funding
- National Natural Science Foundation of China [61673141, 61933007, 61873148, 61773144]
- Outstanding Youth Science Foundation of Heilongjiang Province of China [JC2018001]
- Fundamental Research Foundation for Universities of Heilongjiang Province of China [2019-KYYWF-0215]
- European Regional Development Fund
- Ser Cymru Fellowship [80761U.S.W-059]
- Alexander von Humboldt Foundation of Germany
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This article presents a delay-compensation-based state estimation method for DTVCNs with NIIOs and dynamical bias. A predictive scheme is proposed to compensate for communication delays, a new distributed state estimation approach is introduced, and performance evaluation criteria are proposed.
In this article, a delay-compensation-based state estimation (DCBSE) method is given for a class of discrete time-varying complex networks (DTVCNs) subject to network-induced incomplete observations (NIIOs) and dynamical bias. The NIIOs include the communication delays and fading observations, where the fading observations are modeled by a set of mutually independent random variables. Moreover, the possible bias is taken into account, which is depicted by a dynamical equation. A predictive scheme is proposed to compensate for the influences induced by the communication delays, where the predictive-based estimation mechanism is adopted to replace the delayed estimation transmissions. This article focuses on the problems of estimation method design and performance discussions for addressed DTVCNs with NIIOs and dynamical bias. In particular, a new distributed state estimation approach is presented, where a locally minimized upper bound is obtained for the estimation error covariance matrix and a recursive way is designed to determine the estimator gain matrix. Furthermore, the performance evaluation criteria regarding the monotonicity are proposed from the analytic perspective. Finally, some experimental comparisons are proposed to show the validity and advantages of the new DCBSE approach.
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