4.6 Article

Distributed State-Saturated Recursive Filtering Over Sensor Networks Under Round-Robin Protocol

Journal

IEEE TRANSACTIONS ON CYBERNETICS
Volume 50, Issue 8, Pages 3605-3615

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2932460

Keywords

Protocols; Upper bound; Network topology; Time-varying systems; Covariance matrices; Symmetric matrices; Linear matrix inequalities; Distributed recursive filtering; round-robin (RR) protocol; sensor networks; state saturations; time-varying systems

Funding

  1. National Natural Science Foundation of China [61873059, 61873148, 61673103]
  2. Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning of China
  3. Natural Science Foundation of Shanghai [18ZR1401500]
  4. AHPU Youth Top-Notch Talent Support Program
  5. Natural Science Foundation of Universities in Anhui Province [gxyqZD2019053, KJ2019A0160]
  6. Royal Society of the U.K.
  7. Alexander von Humboldt Foundation of Germany

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This article is concerned with the distributed recursive filtering issue for stochastic discrete time-varying systems subjected to both state saturations and round-robin (RR) protocols over sensor networks. The phenomenon of state saturation is considered to better describe practical engineering. The RR protocol is introduced to mitigate a network burden by determining which component of the sensor node has access to the network at each transmission instant. The purpose of the issue under consideration is to construct a distributed recursive filter such that a certain filtering error covariance's upper bound can be found and the corresponding filter parameters' explicit expression is given with both state saturations and RR protocols. By taking advantage of matrix difference equations, a filtering error covariance's upper bound can be presented and then be minimized by appropriately designing filter parameters. In particular, by using a matrix simplification technique, the sensor network topology's sparseness issue can be tackled. Finally, the feasibility for the addressed filtering scheme is demonstrated by an example.

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