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Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission

Author

Listed:
  • Raquel Caballero-Águila

    (Department of Statistics, University of Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain)

  • Aurora Hermoso-Carazo

    (Department of Statistics, University of Granada, Avda. Fuentenueva, 18071 Granada, Spain)

  • Josefa Linares-Pérez

    (Department of Statistics, University of Granada, Avda. Fuentenueva, 18071 Granada, Spain)

Abstract

In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by one-step autocorrelated and cross-correlated additive noises, and also stochastic uncertainties caused by multiplicative noises and randomly missing measurements in the sensor outputs are considered. At each sampling time, every sensor output is sent to a local processor and, due to some kind of transmission failures, one-step correlated random delays may occur. Using only covariance information, without requiring the evolution model of the signal process, a local least-squares (LS) filter based on the measurements received from each sensor is designed by an innovation approach. All these local filters are then fused to generate an optimal distributed fusion filter by a matrix-weighted linear combination, using the LS optimality criterion. Moreover, a recursive algorithm for the centralized fusion filter is also proposed and the accuracy of the proposed estimators, which is measured by the estimation error covariances, is analyzed by a simulation example.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:5:y:2017:i:3:p:45-:d:110830
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    References listed on IDEAS

    as
    1. R. Caballero-Águila & A. Hermoso-Carazo & J. Linares-Pérez, 2013. "Linear estimation based on covariances for networked systems featuring sensor correlated random delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(7), pages 1233-1244.
    2. 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.
    3. Ran, Chenjian & Deng, Zili, 2012. "Self-tuning weighted measurement fusion Kalman filtering algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2112-2128.
    4. R. Caballero-Águila & A. Hermoso-Carazo & J. Linares-Pérez, 2014. "Covariance-Based Estimation from Multisensor Delayed Measurements with Random Parameter Matrices and Correlated Noises," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-13, June.
    5. Wangyan Li & Zidong Wang & Guoliang Wei & Lifeng Ma & Jun Hu & Derui Ding, 2015. "A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-12, October.
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    Cited by:

    1. María Jesús García-Ligero & Aurora Hermoso-Carazo & Josefa Linares-Pérez, 2022. "Distributed Fusion Estimation in Network Systems Subject to Random Delays and Deception Attacks," Mathematics, MDPI, vol. 10(4), pages 1-17, February.

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