The primary purpose of an Air Traffic Control (ATC) radar system is to detect and track moving aircraft. In order to improve detection, most ATC radar systems suppress unwanted returns caused by reflections from ground and buildings, and from distributed targets such as weather systems and migrating flocks of birds. These unwanted returns, called radar clutter, are suppressed by rejecting slow moving or stationary targets based on their velocity. Unfortunately, such a filtering process discards valuable information about slow moving distributed targets and point targets which move tangentially to the radar. Recently, Haykin and Stehwien have used a Gaussian classifier to classify different radar targets based on their Doppler spectra. In this paper the authors apply the SOFM to the same problem.


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    Title :

    Application of the self-organizing feature map and learning vector quantisation to radar clutter classification


    Additional title:

    Anwendung der selbst-organisierenden Merkmal-Darstellung und der lernenden Vektor-Quantisierung für die Radar-Clutter-Klassifizierung


    Contributors:
    Mann, R. (author) / Haykin, S. (author)


    Publication date :

    1991


    Size :

    4 Seiten, 4 Bilder, 1 Tabelle, 10 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English