IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v11y2015i8p438638.html
   My bibliography  Save this article

Adaptive Localization in Wireless Sensor Network through Bayesian Compressive Sensing

Author

Listed:
  • Zuoxin Xiahou
  • Xiaotong Zhang

Abstract

The estimation of the localization of targets in wireless sensor network is addressed within the Bayesian compressive sensing (BCS) framework. BCS can estimate not only target locations but also noise variance of the environment. Furthermore, we provide adaptive iteration BCS localization (AIBCSL) algorithm, which is based on BCS and will choose measurement sensors according to the environment adaptively with only an initial value, while other frameworks require prior knowledge such as target numbers to choose measurements. AIBCSL suppose that environment noise variance is identical in interested area in a short period of time and change measurement numbers until terminal condition is reached. To suppress noise, we optimize estimation result by energy threshold strategy (ETS), which takes that transmit energy of noise focused on single grid is much lower than signal into consideration. And multisnapshot BCS (MT-BCS) will be explained and lead to a good result in low SNR level situation.

Suggested Citation

  • Zuoxin Xiahou & Xiaotong Zhang, 2015. "Adaptive Localization in Wireless Sensor Network through Bayesian Compressive Sensing," International Journal of Distributed Sensor Networks, , vol. 11(8), pages 438638-4386, August.
    • Handle: RePEc:sae:intdis:v:11:y:2015:i:8:p:438638
    DOI: 10.1155/2015/438638
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1155/2015/438638
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2015/438638?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:sae:intdis:v:11:y:2015:i:8:p:438638. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: SAGE Publications (email available below). General contact details of provider: .

    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.