Automatic rank estimation for practical STAP covariance estimation via an expected likelihood approach

Bosung Kang, Vishal Monga, Muralidhar Rangaswamy, Yuri I. Abramovich

    Research output: Contribution to journalConference article

    3 Citations (Scopus)

    Abstract

    We address the problem of estimation of structured covariance matrices for radar space-time adaptive processing (STAP)1. The knowledge of the interference environment has been exploited in many previous works to accurately estimate a structured disturbance covariance matrix. In particular, it has been shown that employing the rank of clutter subspace, i.e. rank constrained maximum likelihood (RCML) estimation, leads to a practically powerful estimator as well as a closed form solution. While the rank is a very effective constraint, often practical non-idealities make it difficult to be known precisely using physical models. We propose an automatic rank estimation method in STAP via an expected likelihood (EL) approach. We formulate rank estimation as an optimization problem with the expected likelihood criterion and formally prove that the proposed optimization has a unique solution. Through experimental results from a simulation model and KASSPER dataset, we show the RCML estimator with the rank obtained via the EL approach outperforms RCML estimators with the other rank selection methods in the sense of a normalized signal-to-interference and noise ratio (SINR) and the probability of detection.

    Original languageEnglish (US)
    Article number7131212
    Pages (from-to)1388-1393
    Number of pages6
    JournalIEEE National Radar Conference - Proceedings
    Volume2015-June
    Issue numberJune
    DOIs
    StatePublished - Jun 22 2015
    Event2015 IEEE International Radar Conference, RadarCon 2015 - Arlington, United States
    Duration: May 10 2015May 15 2015

    Fingerprint

    Space time adaptive processing
    Covariance matrix
    Maximum likelihood
    Maximum likelihood estimation
    Radar

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

    Cite this

    Kang, Bosung ; Monga, Vishal ; Rangaswamy, Muralidhar ; Abramovich, Yuri I. / Automatic rank estimation for practical STAP covariance estimation via an expected likelihood approach. In: IEEE National Radar Conference - Proceedings. 2015 ; Vol. 2015-June, No. June. pp. 1388-1393.
    @article{2e96328d3a214d49aac8ca49ca4fcbfd,
    title = "Automatic rank estimation for practical STAP covariance estimation via an expected likelihood approach",
    abstract = "We address the problem of estimation of structured covariance matrices for radar space-time adaptive processing (STAP)1. The knowledge of the interference environment has been exploited in many previous works to accurately estimate a structured disturbance covariance matrix. In particular, it has been shown that employing the rank of clutter subspace, i.e. rank constrained maximum likelihood (RCML) estimation, leads to a practically powerful estimator as well as a closed form solution. While the rank is a very effective constraint, often practical non-idealities make it difficult to be known precisely using physical models. We propose an automatic rank estimation method in STAP via an expected likelihood (EL) approach. We formulate rank estimation as an optimization problem with the expected likelihood criterion and formally prove that the proposed optimization has a unique solution. Through experimental results from a simulation model and KASSPER dataset, we show the RCML estimator with the rank obtained via the EL approach outperforms RCML estimators with the other rank selection methods in the sense of a normalized signal-to-interference and noise ratio (SINR) and the probability of detection.",
    author = "Bosung Kang and Vishal Monga and Muralidhar Rangaswamy and Abramovich, {Yuri I.}",
    year = "2015",
    month = "6",
    day = "22",
    doi = "10.1109/RADAR.2015.7131212",
    language = "English (US)",
    volume = "2015-June",
    pages = "1388--1393",
    journal = "IEEE National Radar Conference - Proceedings",
    issn = "1097-5659",
    publisher = "Institute of Electrical and Electronics Engineers Inc.",
    number = "June",

    }

    Automatic rank estimation for practical STAP covariance estimation via an expected likelihood approach. / Kang, Bosung; Monga, Vishal; Rangaswamy, Muralidhar; Abramovich, Yuri I.

    In: IEEE National Radar Conference - Proceedings, Vol. 2015-June, No. June, 7131212, 22.06.2015, p. 1388-1393.

    Research output: Contribution to journalConference article

    TY - JOUR

    T1 - Automatic rank estimation for practical STAP covariance estimation via an expected likelihood approach

    AU - Kang, Bosung

    AU - Monga, Vishal

    AU - Rangaswamy, Muralidhar

    AU - Abramovich, Yuri I.

    PY - 2015/6/22

    Y1 - 2015/6/22

    N2 - We address the problem of estimation of structured covariance matrices for radar space-time adaptive processing (STAP)1. The knowledge of the interference environment has been exploited in many previous works to accurately estimate a structured disturbance covariance matrix. In particular, it has been shown that employing the rank of clutter subspace, i.e. rank constrained maximum likelihood (RCML) estimation, leads to a practically powerful estimator as well as a closed form solution. While the rank is a very effective constraint, often practical non-idealities make it difficult to be known precisely using physical models. We propose an automatic rank estimation method in STAP via an expected likelihood (EL) approach. We formulate rank estimation as an optimization problem with the expected likelihood criterion and formally prove that the proposed optimization has a unique solution. Through experimental results from a simulation model and KASSPER dataset, we show the RCML estimator with the rank obtained via the EL approach outperforms RCML estimators with the other rank selection methods in the sense of a normalized signal-to-interference and noise ratio (SINR) and the probability of detection.

    AB - We address the problem of estimation of structured covariance matrices for radar space-time adaptive processing (STAP)1. The knowledge of the interference environment has been exploited in many previous works to accurately estimate a structured disturbance covariance matrix. In particular, it has been shown that employing the rank of clutter subspace, i.e. rank constrained maximum likelihood (RCML) estimation, leads to a practically powerful estimator as well as a closed form solution. While the rank is a very effective constraint, often practical non-idealities make it difficult to be known precisely using physical models. We propose an automatic rank estimation method in STAP via an expected likelihood (EL) approach. We formulate rank estimation as an optimization problem with the expected likelihood criterion and formally prove that the proposed optimization has a unique solution. Through experimental results from a simulation model and KASSPER dataset, we show the RCML estimator with the rank obtained via the EL approach outperforms RCML estimators with the other rank selection methods in the sense of a normalized signal-to-interference and noise ratio (SINR) and the probability of detection.

    UR - http://www.scopus.com/inward/record.url?scp=84937938517&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84937938517&partnerID=8YFLogxK

    U2 - 10.1109/RADAR.2015.7131212

    DO - 10.1109/RADAR.2015.7131212

    M3 - Conference article

    AN - SCOPUS:84937938517

    VL - 2015-June

    SP - 1388

    EP - 1393

    JO - IEEE National Radar Conference - Proceedings

    JF - IEEE National Radar Conference - Proceedings

    SN - 1097-5659

    IS - June

    M1 - 7131212

    ER -