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.
Type de document :
Communication dans un congrès
SSP 2014, Jun 2014, Gold Coast, Australia. Proceedings of the 2014 IEEE Workshop on Statistical Signal Processing, pp.324 - 327, 2014, 〈10.1109/SSP.2014.6884641〉
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https://hal-supelec.archives-ouvertes.fr/hal-01104073
Contributeur : Virginie Bouvier <>
Soumis le : vendredi 16 janvier 2015 - 09:29:41
Dernière modification le : jeudi 5 avril 2018 - 12:30:11

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Frédéric Pascal, Yacine Chitour. Shrinkage covariance matrix estimator applied to STAP detection. SSP 2014, Jun 2014, Gold Coast, Australia. Proceedings of the 2014 IEEE Workshop on Statistical Signal Processing, pp.324 - 327, 2014, 〈10.1109/SSP.2014.6884641〉. 〈hal-01104073〉

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