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    An optimisation approach to robust estimation of mulitcomponent polynomial phase signals in non-Gaussian noise

    116829_An%20optimisation%20approach%20PID%20116829.pdf (386.1Kb)
    Access Status
    Open access
    Authors
    Pham, DucSon
    Leung, Yee-Hong
    Teo, Kok
    Zoubir, A.
    Date
    2006
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Pham, Duc and Leung, Yee-Hong and Teo, Kok Lay and Zoubir, Abdelhak. 2006. An optimisation approach to robust estimation of mulitcomponent polynomial phase signals in non-Gaussian noise, in Proceedings of the IEEE Asia-Pacific Conference on Circuits and Systems 2006. (ed), IEEE Asia Pacific Conference on Circuits and Systems (APCCAS 2006), Dec 4 2006, pp. 832-835. Singapore: IEEE Computer Society.
    Source Title
    Proceedings of the IEEE Asia-Pacific Conference on circuits and systems 2006
    Source Conference
    IEEE Asia-Pacific Conference on circuits and systems 2006
    DOI
    10.1109/APCCAS.2006.342150
    ISBN
    1424403871
    Faculty
    School of Electrical Engineering and Computing
    Department of Electrical and Computer Engineering
    Faculty of Science and Engineering
    Remarks

    Copyright © 2006 IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/26972
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we address the problem of estimating the parameters of multicomponent polynomial phase signals in impulsive noise which arises in many practical situations. In the presence of this non-standard noise, existing techniques perform can poorly. We propose a nonlinear M-estimation approach to improve the existing techniques. The phase parameters are obtained by solving a nonlinear optimisation problem. A procedure is proposed to find the global minimum at low computational cost. Simulation examples show the proposed method performs better than existing methods

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