Distributionally Robust Optimization for STAP With Finite Samples

Drawing on the minimization of worst-case maximum likelihood (ML) estimation, this article develops a robust inverse clutter-plus-noise covariance matrix (CNCM) estimator for space-time adaptive processing against Gaussian clutter background at low sample support without any prior knowledge. Leverag...

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Vydané v:	IEEE transactions on aerospace and electronic systems Ročník 61; číslo 5; s. 11420 - 11436
Hlavní autori:	Wang, Yalong, Zhang, Xuejing, Wang, Zhihang, Li, Jun, He, Zishu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.10.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Closed form solutions Clutter Computational complexity Covariance matrices Covariance matrix Distributionally robust optimization (DRO) Eigenvalues Eigenvalues and eigenfunctions finite samples Gaussian process inverse clutter-plus-noise covariance matrix estimation Maximum likelihood estimation Optimization Robustness Robustness (mathematics) space–time adaptive processing (STAP) Training Uncertainty Vectors
ISSN:	0018-9251, 1557-9603
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