Constrained ML estimation of structured covariance matrices with applications in radar STAP

Bosung Kang, Vishal Monga, Muralidhar Rangaswamy

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

    2 Scopus citations

    Abstract

    The disturbance covariance matrix in radar space time adaptive processing (STAP) must be estimated from training sample observations. Traditional maximum likelihood (ML) estimators are effective when training is generous but lead to degraded false alarm rates and detection performance in the realistic regime of limited training. We exploit physically motivated constraints such as 1.) rank of the clutter subspace which can be inferred using existing physics based models such as the Brennan rule, and 2.) the Toeplitz constraint that applies to covariance matrices obtained from stationary random processes. We first provide a closed form solution of the rank constrained maximum likelihood (RCML) estimator and then subsequently develop an efficient approximation under joint Toeplitz and rank constraints (EASTR). Experimental results confirm that the proposed EASTR estimators outperform state-of-the-art alternatives in the sense of widely used measures such as the signal to interference and noise ratio (SINR) and probability of detection - particularly when training support is limited.

    Original languageEnglish (US)
    Title of host publication2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
    Pages101-104
    Number of pages4
    DOIs
    StatePublished - Dec 1 2013
    Event2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 - Saint Martin, France
    Duration: Dec 15 2013Dec 18 2013

    Publication series

    Name2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013

    Other

    Other2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
    CountryFrance
    CitySaint Martin
    Period12/15/1312/18/13

    All Science Journal Classification (ASJC) codes

    • Computer Science Applications

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  • Cite this

    Kang, B., Monga, V., & Rangaswamy, M. (2013). Constrained ML estimation of structured covariance matrices with applications in radar STAP. In 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 (pp. 101-104). [6714017] (2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013). https://doi.org/10.1109/CAMSAP.2013.6714017