Latent-space metrics for Complex-Valued VAE out-of-distribution detection under radar clutter
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918217483550720 |
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| author | Rouzoumka, Y. A. Terreaux, E. Morisseau, C. Ovarlez, J. -P. Ren, C. |
| author_facet | Rouzoumka, Y. A. Terreaux, E. Morisseau, C. Ovarlez, J. -P. Ren, C. |
| contents | We investigate complex-valued Variational AutoEncoders (CVAE) for radar Out-Of-Distribution (OOD) detection in complex radar environments. We proposed several detection metrics: the reconstruction error of CVAE (CVAE-MSE), the latent-based scores (Mahalanobis, Kullback-Leibler divergence (KLD)), and compared their performance against the classical ANMF-Tyler detector (ANMF-FP). The performance of all these detectors is analyzed on synthetic and experimental radar data, showing the advantages and the weaknesses of each detector. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19805 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Latent-space metrics for Complex-Valued VAE out-of-distribution detection under radar clutter Rouzoumka, Y. A. Terreaux, E. Morisseau, C. Ovarlez, J. -P. Ren, C. Signal Processing Machine Learning We investigate complex-valued Variational AutoEncoders (CVAE) for radar Out-Of-Distribution (OOD) detection in complex radar environments. We proposed several detection metrics: the reconstruction error of CVAE (CVAE-MSE), the latent-based scores (Mahalanobis, Kullback-Leibler divergence (KLD)), and compared their performance against the classical ANMF-Tyler detector (ANMF-FP). The performance of all these detectors is analyzed on synthetic and experimental radar data, showing the advantages and the weaknesses of each detector. |
| title | Latent-space metrics for Complex-Valued VAE out-of-distribution detection under radar clutter |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2511.19805 |