IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i11p1948-d439722.html
   My bibliography  Save this article

Distributed Fusion Estimation with Sensor Gain Degradation and Markovian Delays

Author

Listed:
  • María Jesús García-Ligero

    (Departamento de Estadística e I. O., Universidad de Granada, Avda Fuentenueva s/n, 18071 Granada, Spain
    These authors contributed equally to this work.)

  • Aurora Hermoso-Carazo

    (Departamento de Estadística e I. O., Universidad de Granada, Avda Fuentenueva s/n, 18071 Granada, Spain
    These authors contributed equally to this work.)

  • Josefa Linares-Pérez

    (Departamento de Estadística e I. O., Universidad de Granada, Avda Fuentenueva s/n, 18071 Granada, Spain
    These authors contributed equally to this work.)

Abstract

This paper investigates the distributed fusion estimation of a signal for a class of multi-sensor systems with random uncertainties both in the sensor outputs and during the transmission connections. The measured outputs are assumed to be affected by multiplicative noises, which degrade the signal, and delays may occur during transmission. These uncertainties are commonly described by means of independent Bernoulli random variables. In the present paper, the model is generalised in two directions: ( i ) at each sensor, the degradation in the measurements is modelled by sequences of random variables with arbitrary distribution over the interval [0, 1]; ( i i ) transmission delays are described using three-state homogeneous Markov chains (Markovian delays), thus modelling dependence at different sampling times. Assuming that the measurement noises are correlated and cross-correlated at both simultaneous and consecutive sampling times, and that the evolution of the signal process is unknown, we address the problem of signal estimation in terms of covariances, using the following distributed fusion method. First, the local filtering and fixed-point smoothing algorithms are obtained by an innovation approach. Then, the corresponding distributed fusion estimators are obtained as a matrix-weighted linear combination of the local ones, using the mean squared error as the criterion of optimality. Finally, the efficiency of the algorithms obtained, measured by estimation error covariance matrices, is shown by a numerical simulation example.

Suggested Citation

  • María Jesús García-Ligero & Aurora Hermoso-Carazo & Josefa Linares-Pérez, 2020. "Distributed Fusion Estimation with Sensor Gain Degradation and Markovian Delays," Mathematics, MDPI, vol. 8(11), pages 1-19, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:1948-:d:439722
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/11/1948/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/11/1948/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hongli Dong & Zidong Wang & Steven X. Ding & Huijun Gao, 2014. "A Survey on Distributed Filtering and Fault Detection for Sensor Networks," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-7, January.
    2. Shang, Yilun, 2016. "Consensus seeking over Markovian switching networks with time-varying delays and uncertain topologies," Applied Mathematics and Computation, Elsevier, vol. 273(C), pages 1234-1245.
    3. R. Caballero-Águila & A. Hermoso-Carazo & J. Linares-Pérez, 2017. "Least-Squares Filtering Algorithm in Sensor Networks with Noise Correlation and Multiple Random Failures in Transmission," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-9, August.
    4. M. J. García-Ligero & A. Hermoso-Carazo & J. Linares-Pérez, 2020. "Least-squares estimators for systems with stochastic sensor gain degradation, correlated measurement noises and delays in transmission modelled by Markov chains," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(4), pages 731-745, March.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.
    2. Huijuan Zhao & Jiapeng Xu & Fangfei Li, 2022. "Event-Triggered Extended Kalman Filtering Analysis for Networked Systems," Mathematics, MDPI, vol. 10(6), pages 1-12, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhao, Lin & Jia, Yingmin & Yu, Jinpeng & Du, Junping, 2017. "H∞ sliding mode based scaled consensus control for linear multi-agent systems with disturbances," Applied Mathematics and Computation, Elsevier, vol. 292(C), pages 375-389.
    2. Cheng-Yu Tang & Jun-Ting Lin, 2019. "Bidirectional Power Flow Control of a Multi Input Converter for Energy Storage System," Energies, MDPI, vol. 12(19), pages 1-16, September.
    3. 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.
    4. Li, Hongjie & Zhu, Yinglian & jing, Liu & ying, Wang, 2018. "Consensus of second-order delayed nonlinear multi-agent systems via node-based distributed adaptive completely intermittent protocols," Applied Mathematics and Computation, Elsevier, vol. 326(C), pages 1-15.
    5. Zhao, Lin & Yu, Jinpeng & Lin, Chong & Yu, Haisheng, 2017. "Distributed adaptive fixed-time consensus tracking for second-order multi-agent systems using modified terminal sliding mode," Applied Mathematics and Computation, Elsevier, vol. 312(C), pages 23-35.
    6. Qi Yang & Xuan Zhang & Jingfeng Qian & Qiang Ye, 2018. "An anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks," International Journal of Distributed Sensor Networks, , vol. 14(5), pages 15501477187, May.
    7. 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.
    8. Ismi Rosyiana Fitri & Jung-Su Kim, 2020. "A Nonlinear Model Predictive Control with Enlarged Region of Attraction via the Union of Invariant Sets," Mathematics, MDPI, vol. 8(11), pages 1-15, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:1948-:d:439722. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.