Robust High-Dimensional MVDR Beamforming under Heavy-Tailed Noise via Spiked Covariance Modeling

Liusha Yang; Shuqi Chai; Manyou Ma

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arXiv · 2609.27552

Robust High-Dimensional MVDR Beamforming under Heavy-Tailed Noise via Spiked Covariance Modeling

Liusha Yang·Shuqi Chai·Manyou Ma

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Abstract

This paper proposes a robust high-dimensional minimum variance distortionless response (MVDR) beamforming method for array observations corrupted by heavy-tailed noise. The proposed approach constructs an MVDR-oriented precision matrix estimator by combining Maronna's robust scatter estimator with spiked covariance modeling. Using tools from random matrix theory, we derive a deterministic equivalent of the MVDR output power and obtain asymptotically optimal shrinkage weights for the dominant signal subspace. A fully sample-based implementation is then developed for practical beamformer design. Numerical simulations under elliptically distributed noise demonstrate that the proposed beamformer achieves stronger interference suppression and higher output SINR than competing methods in high-dimensional and implusive noise regimes.

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BibTeX RIS

Liusha Yang, Shuqi Chai, Manyou Ma. 2026-09-23. Robust High-Dimensional MVDR Beamforming under Heavy-Tailed Noise via Spiked Covariance Modeling. https://arxiv.org/abs/2609.27552

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