Rank-Constrained Maximum Likelihood Estimation of Structured Covariance Matrices

@article{Kang2014RankConstrainedML,
  title={Rank-Constrained Maximum Likelihood Estimation of Structured Covariance Matrices},
  author={Bosung Kang and Vishal Monga and Muralidhar Rangaswamy},
  journal={IEEE Transactions on Aerospace and Electronic Systems},
  year={2014},
  volume={50},
  pages={501-515}
}
This paper develops and analyzes the performance of a structured covariance matrix estimate for the important practical problem of radar space-time adaptive processing in the face of severely limited training data. Traditional maximum likelihood (ML) estimators are effective when training data are abundant, but they lead to poor estimates, degraded false alarm rates, and detection loss in the realistic regime of limited training. The problem is exacerbated by recent advances, which have led to… CONTINUE READING
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