Distributed -consensus filtering in sensor networks with multiple missing measurements: The finite-horizon case
✍ Scribed by Bo Shen; Zidong Wang; Y.S. Hung
- Publisher
- Elsevier Science
- Year
- 2010
- Tongue
- English
- Weight
- 639 KB
- Volume
- 46
- Category
- Article
- ISSN
- 0005-1098
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✦ Synopsis
This paper is concerned with a new distributed H ∞ -consensus filtering problem over a finite-horizon for sensor networks with multiple missing measurements. The so-called H ∞ -consensus performance requirement is defined to quantify bounded consensus regarding the filtering errors (agreements) over a finite-horizon. A set of random variables are utilized to model the probabilistic information missing phenomena occurring in the channels from the system to the sensors. A sufficient condition is first established in terms of a set of difference linear matrix inequalities (DLMIs) under which the expected H ∞ -consensus performance constraint is guaranteed. Given the measurements and estimates of the system state and its neighbors, the filter parameters are then explicitly parameterized by means of the solutions to a certain set of DLMIs that can be computed recursively. Subsequently, two kinds of robust distributed H ∞ -consensus filters are designed for the system with norm-bounded uncertainties and polytopic uncertainties. Finally, two numerical simulation examples are used to demonstrate the effectiveness of the proposed distributed filters design scheme.