Out-of-Distribution Radar Detection with Complex VAEs: Theory, Whitening, and ANMF Fusion
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910001118838784 |
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| author | Rouzoumka, Yadang Alexis Pinsolle, Jean Terreaux, Eugénie Morisseau, Christèle Ovarlez, Jean-Philippe Ren, Chengfang |
| author_facet | Rouzoumka, Yadang Alexis Pinsolle, Jean Terreaux, Eugénie Morisseau, Christèle Ovarlez, Jean-Philippe Ren, Chengfang |
| contents | 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 false-alarm rate Pfa, with the most notable improvements observed under whitening. We further integrate the CVAE with the ANMF through a weighted log-p fusion rule at the decision level, attaining enhanced robustness in strongly non-Gaussian clutter and enabling empirically calibrated Pfa control under H0. Overall, the results demonstrate that statistical normalization combined with complex-valued generative modeling substantively improves detection in realistic sea-clutter conditions, and that the fused CVAE-ANMF scheme constitutes a competitive alternative to established model-based detectors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18677 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Out-of-Distribution Radar Detection with Complex VAEs: Theory, Whitening, and ANMF Fusion Rouzoumka, Yadang Alexis Pinsolle, Jean Terreaux, Eugénie Morisseau, Christèle Ovarlez, Jean-Philippe Ren, Chengfang Machine Learning 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 false-alarm rate Pfa, with the most notable improvements observed under whitening. We further integrate the CVAE with the ANMF through a weighted log-p fusion rule at the decision level, attaining enhanced robustness in strongly non-Gaussian clutter and enabling empirically calibrated Pfa control under H0. Overall, the results demonstrate that statistical normalization combined with complex-valued generative modeling substantively improves detection in realistic sea-clutter conditions, and that the fused CVAE-ANMF scheme constitutes a competitive alternative to established model-based detectors. |
| title | Out-of-Distribution Radar Detection with Complex VAEs: Theory, Whitening, and ANMF Fusion |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.18677 |