• Login
    View Item 
    •   FFI Publications Home
    • Publications
    • Rapporter
    • View Item
    •   FFI Publications Home
    • Publications
    • Rapporter
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Communication signal generation and automatic classification with detection of unknown formats using neural networks

    View/Open
    04-02934.pdf (3.986Mb)
    Date
    2004
    Author
    Iversen, Alexander
    Kårstad, Jørn
    Metadata
    Show full item record
    Abstract
    The performance of algorithms for signal classification is often characterized according to their ability to correctly classify various signals belonging to a set of known signal formats. However, in a non-co-operative setting it may also be expected that the classifier will be presented with signals of unknown formats, i.e. signal formats not belonging to any of the categories in the training set. Typically, a neural network classifier will classify an unknown signal to the known signal format that it resembles the most. In EW applications, however, the ability to detect unknown signal formats is imperative. In this report a hybrid classifier scheme is proposed. The performance of this hybrid classifier is tested both with respect to its ability to correctly classify known signal formats as well as its ability to detect outliers. The results indicate that this two-stage classifier is able to detect most of the unknown signal formats, but at the expense of misclassifying some of the known signals. The trade-off between correct classification and detection of new signal formats can be adjusted by setting an appropriate CIV (Class Inherence Verification) threshold. In addition to discussing the classification algorithms and their performance the procedure for generation of test signals is also described.
    URI
    http://hdl.handle.net/20.500.12242/1767
    Collections
    • Rapporter

    Browse

    All of FFI PublicationsCommunities & CollectionsBy Issue DateAuthorsTitlesThis CollectionBy Issue DateAuthorsTitles

    My Account

    Login

    CONTACT US

    • FFI Kjeller
      FFI, PO Box 25, 2027 Kjeller
    • Office Address: Instituttvn 20,
      Phone 63 80 70 00
    • biblioteket@ffi.no

    HELPFUL

    • About FFI
    • Career
    • Reports

    Sitemap

    • About cookies (cookies)
    • Newsletter
    • Sitemap

    FOLLOW US

     

     

    © Copyright Norwegian Defence Research Establishment
    Powered by KnowledgeArc