Reliable H ∞ filtering for stochastic spatial–temporal systems with sensor saturations and failures

Dong Wang; Bo Shen; Zidong Wang; Fuad Eid Alsaadi; Abdullah M. Dobaie

Reliable H _∞ filtering for stochastic spatial–temporal systems with sensor saturations and failures

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Author(s): Dong Wang ¹ ; Bo Shen ¹ ; Zidong Wang ^{2, 3} ; Fuad Eid Alsaadi ³ ; Abdullah M. Dobaie ³
- Affiliations: 1: School of Information Science and Technology, Donghua University, Shanghai 200051, People's Republic of China ;
  2: Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK ;
  3: Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Source: Volume 9, Issue 17, 19 November 2015, p. 2590 – 2597
DOI: 10.1049/iet-cta.2015.0442 , Print ISSN 1751-8644, Online ISSN 1751-8652

Received 12/02/2015, Accepted 14/07/2015, Published

This study is concerned with the reliable H _∞ filtering problem for a class of stochastic spatial–temporal systems with sensor saturations and failures. Different from the continuous spatial–temporal systems, the dynamic behaviour of the system under consideration evolves in a discrete rectangular region. The aim of this study is to estimate the system states through the measurements received from a set of sensors located at some specified points. In order to cater for more realistic signal transmission process, the phenomena of sensor saturations and sensor failures are taken into account. By using the vector reorganisation approach, the spatial–temporal system is first transformed into an equivalent ordinary differential dynamic system. Then, a filter is constructed and a sufficient condition is obtained under which the filtering error dynamics is asymptotically stable in probability and the H _∞ performance requirement is met. On the basis of the analysis results, the desired reliable H _∞ filter is designed. Finally, an illustrative example is given to show the effectiveness of the proposed filtering scheme.

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Reliable H _∞ filtering for stochastic spatial–temporal systems with sensor saturations and failures

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