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• W2626099489 abstract "This paper considers the estimation of structured clutter-plus-noise covariance matrix (CNCM) in space-time adaptive processing (STAP) for airborne radar systems. Specially, the CNCM is modeled as a sum of Kronecker products involving two lower dimensional temporal and spatial covariance matrices, with persymmetric structure. Then, resorting to the Kronecker Product principal component analysis (KronPCA) based algorithm, a novel estimator of the high dimensional and persymmetric CNCM is proposed. Furthermore, the proposed method explores the sparse factors of the CNCM and recovers low-rank persymmetric covariance matrices. At analysis stage, we assess the performance of the proposed algorithm through simulations." @default.
• W2626099489 created "2017-06-23" @default.
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• W2626099489 date "2017-05-01" @default.
• W2626099489 modified "2023-09-27" @default.
• W2626099489 title "Kronecker Product PCA for structured covariance matrix of airborne radar STAP" @default.
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• W2626099489 doi "https://doi.org/10.1109/radar.2017.7944352" @default.
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