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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Veröffentlicht in:	IEEE transactions on aerospace and electronic systems Jg. 61; H. 5; S. 11420 - 11436
Hauptverfasser:	Wang, Yalong, Zhang, Xuejing, Wang, Zhihang, Li, Jun, He, Zishu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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