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arXiv · 2603.00633

FDR Control for Complex-Valued Data with Application in Single Snapshot Multi-Source Detection and DOA Estimation

Fabian Scheidt·Jasin Machkour·Michael Muma
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Abstract

False discovery rate (FDR) control is a popular approach for maintaining the integrity of statistical analyses, especially in high-dimensional data settings, where multiple comparisons increase the risk of false positives. FDR control has been extensively researched for real-valued data. However, the complex data case, which is relevant for many signal processing applications, remains widely unexplored. We therefore present a fast and FDR-controlling variable selector for complex-valued high-dimensional data. The proposed Complex-Valued Terminating-Random Experiments (CT-Rex) selector controls a user-defined target FDR while maximizing the number of selected variables. This is achieved by optimally fusing the solutions of multiple early terminated complex-valued random experiments. We benchmark the performance in sparse complex regression simulation studies and showcase an example of FDR-controlled compressed-sensing-based single snapshot multi-source detection and direction of arrival (DOA) estimation. The proposed work applies to a wide range of research areas, such as DOA estimation, communications, mechanical engineering, and magnetic resonance imaging, bridging a critical gap in signal processing for complex-valued data.

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Fabian Scheidt, Jasin Machkour, Michael Muma. 2026-02-28. FDR Control for Complex-Valued Data with Application in Single Snapshot Multi-Source Detection and DOA Estimation. https://doi.org/10.1109/icassp49660.2025.10889705

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