Abstract and Applied Analysis

Optimal Kalman Filtering for a Class of State Delay Systems with Randomly Multiple Sensor Delays

Dongyan Chen and Long Xu

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Abstract

The optimal Kalman filtering problem is investigated for a class of discrete state delay stochastic systems with randomly multiple sensor delays. The phenomenon of measurement delay occurs in a random way and the delay rate for each sensor is described by a Bernoulli distributed random variable with known conditional probability. Based on the innovative analysis approach and recursive projection formula, a new linear optimal filter is designed such that, for the state delay and randomly multiple sensor delays with different delay rates, the filtering error is minimized in the sense of mean square and the filter gain is designed by solving the recursive matrix equation. Finally, a simulation example is given to illustrate the feasibility and effectiveness of the proposed filtering scheme.

Article information

Source
Abstr. Appl. Anal., Volume 2014 (2014), Article ID 716716, 10 pages.

Dates
First available in Project Euclid: 6 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1412606640

Digital Object Identifier
doi:10.1155/2014/716716

Mathematical Reviews number (MathSciNet)
MR3200802

Zentralblatt MATH identifier
07022936

Citation

Chen, Dongyan; Xu, Long. Optimal Kalman Filtering for a Class of State Delay Systems with Randomly Multiple Sensor Delays. Abstr. Appl. Anal. 2014 (2014), Article ID 716716, 10 pages. doi:10.1155/2014/716716. https://projecteuclid.org/euclid.aaa/1412606640


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