Out-of-Distribution Radar Detection with Complex VAEs: Theory, Whitening, and ANMF Fusion
Yadang Alexis Rouzoumka, Jean Pinsolle, Eug\'enie Terreaux, Christ\`ele Morisseau, Jean-Philippe Ovarlez, Chengfang Ren

TL;DR
This paper introduces a complex-valued variational autoencoder (CVAE) for detecting weak signals in maritime radar clutter, demonstrating improved detection performance over classical methods through simulation and real data analysis.
Contribution
It proposes a novel CVAE-based out-of-distribution detector for complex radar signals, with whitening and fusion techniques to enhance robustness and detection accuracy.
Findings
CVAE outperforms classical detectors in detection probability.
Whitening significantly improves detection performance.
Fusion of CVAE with ANMF enhances robustness in non-Gaussian clutter.
Abstract
We investigate the detection of weak complex-valued signals immersed in non-Gaussian, range-varying interference, with emphasis on maritime radar scenarios. The proposed methodology exploits a Complex-valued Variational AutoEncoder (CVAE) trained exclusively on clutter-plus-noise to perform Out-Of-Distribution detection. By operating directly on in-phase / quadrature samples, the CVAE preserves phase and Doppler structure and is assessed in two configurations: (i) using unprocessed range profiles and (ii) after local whitening, where per-range covariance estimates are obtained from neighboring profiles. Using extensive simulations together with real sea-clutter data from the CSIR maritime dataset, we benchmark performance against classical and adaptive detectors (MF, NMF, AMF-SCM, ANMF-SCM, ANMF-Tyler). In both configurations, the CVAE yields a higher detection probability Pd at matched…
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Taxonomy
TopicsRadar Systems and Signal Processing · Maritime Navigation and Safety · Distributed Sensor Networks and Detection Algorithms
