Performance Of Radial-Basis Function Networks For Direction Of Arrival Estimation With Antenna Arrays

    Authors

    A. H. ElZooghby; C. G. Christodoulou;M. Georgiopoulos

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Antennas Propag.

    Keywords

    antenna arrays; direction of arrival estimation; Engineering, Electrical & Electronic; Telecommunications

    Abstract

    The problem of direction of arrival (DOA) estimation of mobile users using linear antenna arrays is addressed, To reduce the computational complexity of superresolution algorithms, e.g. multiple signal classification (MUSIC), the DOA problem is approached as a mapping which can be modeled using a suitable artificial neural network trained with input output pairs, This paper discusses the application of a three-layer radial-basis function neural network (RBFNN), which can learn multiple source-direction findings of a six-element array, The network weights are modified using the normalized cumulative delta rule, The performance of this network is compared to that of the MUSIC algorithm for both uncorrelated and correlated signals, It Is also shown that the RBFNN substantially reduced the CPU time for the DOA estimation computations.

    Journal Title

    Ieee Transactions on Antennas and Propagation

    Volume

    45

    Issue/Number

    11

    Publication Date

    1-1-1997

    Document Type

    Article

    Language

    English

    First Page

    1611

    Last Page

    1617

    WOS Identifier

    WOS:A1997YE07900006

    ISSN

    0018-926X

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