Importance sampling for characterizing STAP detectors

R. Srinivasan, M. Rangaswamy

    Research output: Contribution to journalArticleAcademicpeer-review

    16 Citations (Scopus)


    This paper describes the development of adaptive importance sampling techniques for estimating false alarm probabilities of detectors that use space-time adaptive processing (STAP) algorithms. Fast simulation using importance sampling methods has been notably successful in the study of conventional constant false alarm rate (CFAR) radar detectors, and in several other applications. The principal objectives here are to examine the viability of using these methods for STAP detectors, develop them into powerful analysis and design algorithms and, in the long term, use them for synthesizing novel detection structures. The adaptive matched filter (AMF) detector has been analyzed successfully using fast simulation. Of two biasing methods considered, one is implemented and shown to yield good results. The important problem of detector threshold determination is also addressed, with matching outcome. As an illustration of the power of these methods, two variants of the square-law AMF detector that are thought to be robust under heterogeneous clutter conditions have also been successfully investigated. These are the envelope-law and geometric-mean STAP detectors. Their CFAR property is established and performance evaluated. It turns out the variants have detection performances better than those of the AMF detector for training data contaminated by interferers. In summary, the work reported here paves the way for development of advanced estimation techniques that can facilitate design of powerful and robust detection algorithms.
    Original languageUndefined
    Article number10.1109/TAES.2007.357133
    Pages (from-to)273-285
    Number of pages14
    JournalIEEE transactions on aerospace and electronic systems
    Issue number1
    Publication statusPublished - 1 Jan 2007


    • IR-63871
    • EWI-8803
    • METIS-247026

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