On the practical merits of rank constrained ML estimator of structured covariance matrices

Bosung Kang, Vishal Monga, Muralidhar Rangaswamy

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Scopus citations

    Abstract

    Estimation of the disturbance or interference covariance matrix plays a central role on radar target detection in the presence of clutter, noise and jammer. The disturbance covariance matrix should be inferred from training sample observations in practice. Traditional maximum likelihood (ML) estimators lead degraded false alarm and detection performance in the realistic regime of limited training. For this reason, informed estimators have been actively researched. Recently, a new estimator [1] that explicitly incorporates rank information of the clutter subspace was proposed. This paper reports significant new analytical and experimental investigations on the rank-constrained maximum likelihood (RCML) estimator. First, we show that the RCML estimation problem formulated in [1] has a closed form. Next, we perform new and rigorous experimental evaluation in the form of reporting: 1.) probability of detection versus signal to noise ratio (SNR), and 2.) SINR performance under heterogeneous (target corrupted) training data. In each case, we compare against widely used existing estimators and show that exploiting the rank information has significant practical merits in robust estimation.

    Original languageEnglish (US)
    Title of host publicationIEEE Radar Conference 2013
    Subtitle of host publication"The Arctic - The New Frontier", RadarCon 2013
    DOIs
    StatePublished - Oct 7 2013
    Event2013 IEEE Radar Conference: "The Arctic - The New Frontier", RadarCon 2013 - Ottawa, ON, Canada
    Duration: Apr 29 2013May 3 2013

    Publication series

    NameIEEE National Radar Conference - Proceedings
    ISSN (Print)1097-5659

    Other

    Other2013 IEEE Radar Conference: "The Arctic - The New Frontier", RadarCon 2013
    CountryCanada
    CityOttawa, ON
    Period4/29/135/3/13

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

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