A neural network-based smart antenna for multiple source tracking

    Authors

    A. H. El Zooghby; C. G. Christodoulou;M. Georgiopoulos

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Antennas Propag.

    Keywords

    direction-of-arrival estimation; multibeam; antennas; neural networks; PERFORMANCE; ARRAYS; Engineering, Electrical & Electronic; Telecommunications

    Abstract

    This paper considers the problem of multiple-source tracking with neural network-based smart antennas for wireless terrestrial and satellite mobile communications. The neural multiple-source tracking (N-MUST) algorithm is based on an architecture of a family of radial basis function neural networks (RBFNN) to perform both detection and direction of arrival (DOG) estimation. The field of view of the antenna array is divided into spatial angular sectors, which are in turn assigned to a different pair of RBFNN's. When a network detects one or more sources in the first stage, the corresponding second stage network(s) are activated to perform the DOA estimation. Simulation results are performed to investigate the performance of the algorithm for various angular separations, with sources of random relative signal-to-noise ratio and when the system suffers from a doppler spread.

    Journal Title

    Ieee Transactions on Antennas and Propagation

    Volume

    48

    Issue/Number

    5

    Publication Date

    1-1-2000

    Document Type

    Article

    Language

    English

    First Page

    768

    Last Page

    776

    WOS Identifier

    WOS:000088410300016

    ISSN

    0018-926X

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