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Linear estimation based on covariances for networked systems featuring sensor correlated random delays

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  • R. Caballero-Águila
  • A. Hermoso-Carazo
  • J. Linares-Pérez

Abstract

This article is concerned with the least-squares (LS) linear estimation problem of discrete-time signals from noisy measurements coming from multiple randomly delayed sensors with different delay characteristics. It is assumed that the Bernoulli random variables characterising the measurement delays are correlated at consecutive sampling times. Using an innovation approach, recursive linear filtering and smoothing (fixed-point and fixed-interval) algorithms are obtained without requiring the state-space model generating the signal, but only the covariance functions of the signal and the noise, the delay probabilities and the correlation function of the Bernoulli variables. Also, recursive formulas for filtering and fixed-point smoothing error covariance matrices are obtained to measure the goodness of the proposed estimators.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:tsysxx:v:44:y:2013:i:7:p:1233-1244
    DOI: 10.1080/00207721.2012.659709
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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.
    2. Yonggang Zhang & Yulong Huang & Ning Li & Lin Zhao, 2016. "Particle filter with one-step randomly delayed measurements and unknown latency probability," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(1), pages 209-221, January.

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