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    Adaptive array beam forming using a combined RLS-LMS algorithm

    119210_Adaptive%20Array%20Beam%20KS%20Chung.pdf (275.7Kb)
    Access Status
    Open access
    Authors
    Srar, Jalal
    Chung, Kah-Seng
    Date
    2008
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Srar, Jalal and Chung, Kah-Seng. 2008. Adaptive array beam forming using a combined RLS-LMS algorithm, in APCC 2008 Technical Program Committee. (ed), The 14th Asia-Pacific Conference on Communications (APCC 2008), Oct 14 2008. Tokyo, Japan: Institute of Electrical and Electronics Engineers (IEEE).
    Source Title
    Proceedings of The 14th Asia-Pacific Conference on Communications (APCC 2008)
    Source Conference
    The 14th Asia-Pacific Conference on Communications (APCC 2008)
    Additional URLs
    http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4773748
    ISBN
    9784885522321
    Faculty
    School of Electrical Engineering and Computing
    Science and Engineering
    School
    Department of Electrical and Computer Engineering
    Remarks

    Copyright © 2008 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/20.500.11937/31618
    Collection
    • Curtin Research Publications
    Abstract

    A new adaptive algorithm, called RLMS, which combines the use of recursive least square (RLS) and least mean square (LMS), is proposed for array beam forming. The convergence of the RLMS algorithm is analyzed, in terms of mean square error, in the presence of additive white Gaussian noise. Computer simulation results show that the convergence performance of RLMS is superior to either RLS or LMS operating on its own. Furthermore, the convergence of RLMS is quite insensitive to changes in either signal-to-noise ratio, or the initial value of the input correlation matrix for the RLS section, or the step size adopted for the LMS section.

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