filtering with randomly occurring sensor saturations and missing measurements

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• 作者：Zidong Wang^a ; ^b ; ¹ ; ^{Zidong.Wang@brunel.ac.uk}  ; [Author Vitae] ; Bo Shen^a ; ^{shenbodh@gmail.com}  ; [Author Vitae] ; Xiaohui Liu^b ; ^{Xiaohui.Liu@brunel.ac.uk}  ; [Author Vitae]
• 关键词：Randomly occurring sensor saturations ; Missing measurements ; Nonlinear systems ; Regional H&infin ; filters ; Random incomplete information
• 刊名：Automatica
• 出版年：2012
• 期刊代码：13_00051098
• 类别：cp
• 出版时间：March, 2012
• 卷：48
• 期：3
• 页码：556-562
• 文件大小：397 K

摘要

In this paper, the filtering problem is investigated for a class of nonlinear systems with randomly occurring incomplete information. The considered incomplete information includes both the sensor saturations and the missing measurements. A new phenomenon of sensor saturation, namely, randomly occurring sensor saturation (ROSS), is put forward in order to better reflect the reality in a networked environment such as sensor networks. A novel sensor model is then established to account for both the ROSS and missing measurement in a unified representation by using two sets of Bernoulli distributed white sequences with known conditional probabilities. Based on this sensor model, a regional filter with a certain ellipsoid constraint is designed such that the filtering error dynamics is locally mean-square asymptotically stable and the -norm requirement is satisfied. Note that the regional gain filtering feature is specifically developed for the random saturation nonlinearity. The characterization of the desired filter gains is derived in terms of the solution to a convex optimization problem that can be easily solved by using the semi-definite program method. Finally, a simulation example is employed to show the effectiveness of the filtering scheme proposed in this paper.