Efficient approximation of structured covariance under joint Toeplitz and rank constraints

Bosung Kang, Vishal Monga, Muralidhar Rangaswamy

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

    2 Scopus citations

    Abstract

    The disturbance (clutter plus noise and jamming) covariance matrix which plays a central role in radar space time adaptive processing (STAP) should be estimated from sample training observations in practice. Traditional maximum likelihood (ML) estimators lead to degraded false alarm and detection performance in the realistic regime of limited training. Therefore constrained ML estimation has received much attention which exploits structure and other properties that a disturbance covariance matrix exhibits. In this paper 1, we derive a new covariance estimator for STAP that jointly considers a Toeplitz structure and a rank constraint on the clutter component. Past work has shown that in the regime of low training, even handling each constraint individually is hard and techniques often resort to slow numerically based solutions. Our proposed solution leverages a recent advance called rank constrained ML estimator (RCML) of structured covariances to build a computationally friendly approximation that involves a cascade of two closed form solutions. Experimental investigation shows that the proposed estimator outperforms state of the art alternatives in the sense of: 1.) normalized signal to interference and noise ratio (SINR), and 2.) probability of detection versus signal to noise ratio (SNR).

    Original languageEnglish (US)
    Title of host publicationConference Record of the 47th Asilomar Conference on Signals, Systems and Computers
    PublisherIEEE Computer Society
    Pages692-696
    Number of pages5
    ISBN (Print)9781479923908
    DOIs
    StatePublished - 2013
    Event2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
    Duration: Nov 3 2013Nov 6 2013

    Publication series

    NameConference Record - Asilomar Conference on Signals, Systems and Computers
    ISSN (Print)1058-6393

    Other

    Other2013 47th Asilomar Conference on Signals, Systems and Computers
    Country/TerritoryUnited States
    CityPacific Grove, CA
    Period11/3/1311/6/13

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Computer Networks and Communications

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