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Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays

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  • Dongyan Chen
  • Yonglong Yu
  • Long Xu
  • Xiaohui Liu

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.

Suggested Citation

  • Dongyan Chen & Yonglong Yu & Long Xu & Xiaohui Liu, 2015. "Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-11, October.
    • Handle: RePEc:hin:jnddns:809734
    DOI: 10.1155/2015/809734
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    Cited by:

    1. Raquel Caballero-Águila & Aurora Hermoso-Carazo & Josefa Linares-Pérez, 2017. "Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission," Mathematics, MDPI, vol. 5(3), pages 1-20, September.

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