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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Vydáno v:	IEEE transactions on aerospace and electronic systems Ročník 61; číslo 5; s. 11420 - 11436
Hlavní autoři:	Wang, Yalong, Zhang, Xuejing, Wang, Zhihang, Li, Jun, He, Zishu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.10.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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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Shrnutí:	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. Leveraging the nonconvex uncertainty set for CNCMs, we formulate a distributionally robust optimization-based ML estimation problem with the Wasserstein metric. We validate that the resulting nonconvex problem is algorithmically tractable. To achieve this, we reformulate the problem as a finite-dimensional semidefinite program. To pursue lower computational complexity, we establish a closed-form solution framework by imposing the rotation-equivariant property. We theoretically prove the existence and uniqueness of the solution and address the challenge of adaptively choosing the uncertainty set size. Importantly, the solution composes a nonlinear shrinkage estimator that inherently preserves the order of sample eigenvalues without additional operations. Experiments with both simulated and measured clutter data confirm the superiority of the proposed estimator in terms of estimation accuracy and robustness.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0018-9251 1557-9603
DOI:	10.1109/TAES.2025.3566360