Expected likelihood approach for determining constraints in covariance estimation

Bosung Kang, Vishal Monga, Muralidhar Rangaswamy, Yuri Abramovich

    Research output: Contribution to journalArticlepeer-review

    5 Scopus citations


    Recent covariance estimation methods for radar space-time adaptive processing exploit practical constraints such as the rank of clutter subspace and the condition number of disturbance covariance to estimate accurate covariance even when training is not generous. While rank and condition number are very effective constraints, often practical nonidealities make it difficult to know them precisely using physical models. Therefore, we propose a method to determine constraints in covariance estimation for radar space-time adaptive processing via an expected likelihood approach. We analyze three cases of constraints: 1) a rank constraint, 2) both rank and noise power constraints, and 3) a condition number constraint. In each case, we formulate precise constraint determination as an optimization problem. For each of the three cases, we derive new analytical results which allow for computationally efficient, practical ways of determining these constraints with formal proofs. Through experimental results from a simulation model and the KASSPER data set, we show that the estimator with optimal constraints obtained by the expected likelihood approach outperforms state-of-the-art alternatives.

    Original languageEnglish (US)
    Article number7812866
    Pages (from-to)2139-2156
    Number of pages18
    JournalIEEE Transactions on Aerospace and Electronic Systems
    Issue number5
    StatePublished - Oct 2016

    All Science Journal Classification (ASJC) codes

    • Aerospace Engineering
    • Electrical and Electronic Engineering


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