Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays
Dongyan Chen
(Department of Applied Mathematics, Harbin University of Science and Technology, Harbin 150080, China)
Yonglong Yu
(Department of Applied Mathematics, Harbin University of Science and Technology, Harbin 150080, China)
Long Xu
(Department of Applied Mathematics, Harbin University of Science and Technology, Harbin 150080, China)
Xiaohui Liu
(Department of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, UK)
Journal: Discrete Dynamics in Nature and Society
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• Wiley
• ISSN: 1026-0226
Abstract
This paper is concerned with the optimal Kalman filtering problem for a class of discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities are introduced to characterize the phenomena of the random two-step sensor delays which may happen during the data transmission. By using the state augmentation approach and innovation analysis technique, an optimal Kalman filter is constructed for the augmented system in the sense of the minimum mean square error (MMSE). Subsequently, the optimal Kalman filtering is derived for corresponding augmented system in initial instants. Finally, a simulation example is provided to demonstrate the feasibility and effectiveness of the proposed filtering method.
Dongyan Chen, Yonglong Yu, Long Xu, et al. (2015). "Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays". Discrete Dynamics in Nature and Society. https://doi.org/10.1155/2015/809734