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Shrinkage covariance matrix estimator applied to STAP detection

Abstract : In the context of robust covariance matrix estimation, this work generalizes the shrinkage covariance matrix estimator introduced in [1, 2]. The shrinkage method is a way to improve and to regularize the Tyler's estimator [3, 4]. This paper proves that the shrinkage estimator does not require any trace constraint to be well-defined, as it has been previously developed in [1]. The existence and the uniqueness of this estimator, defined through a fixed point equation, is given according to the values of the shrinkage parameter. Moreover, it is shown that the shrinkage estimator converges to a particular Tyler's estimator when the shrinkage parameter tends to 0. Then, results on real STAP data show the improvement of using such a robust estimator to perform target detection in cases where the data sample size is less than the dimension.
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Contributor : Virginie Bouvier <>
Submitted on : Friday, January 16, 2015 - 9:29:41 AM
Last modification on : Wednesday, September 16, 2020 - 4:45:28 PM



Frédéric Pascal, Yacine Chitour. Shrinkage covariance matrix estimator applied to STAP detection. SSP 2014, IEEE, Jun 2014, Gold Coast, Australia. pp.324 - 327, ⟨10.1109/SSP.2014.6884641⟩. ⟨hal-01104073⟩



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