Out-of-Distribution Radar Detection in Compound Clutter and Thermal Noise through Variational Autoencoders
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910862386659328 |
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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 | This paper presents a novel approach to radar target detection using Variational AutoEncoders (VAEs). Known for their ability to learn complex distributions and identify out-ofdistribution samples, the proposed VAE architecture effectively distinguishes radar targets from various noise types, including correlated Gaussian and compound Gaussian clutter, often combined with additive white Gaussian thermal noise. Simulation results demonstrate that the proposed VAE outperforms classical adaptive detectors such as the Matched Filter and the Normalized Matched Filter, especially in challenging noise conditions, highlighting its robustness and adaptability in radar applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04861 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Out-of-Distribution Radar Detection in Compound Clutter and Thermal Noise through Variational Autoencoders Rouzoumka, Y A Terreaux, E Morisseau, C Ovarlez, J. -P Ren, C Machine Learning This paper presents a novel approach to radar target detection using Variational AutoEncoders (VAEs). Known for their ability to learn complex distributions and identify out-ofdistribution samples, the proposed VAE architecture effectively distinguishes radar targets from various noise types, including correlated Gaussian and compound Gaussian clutter, often combined with additive white Gaussian thermal noise. Simulation results demonstrate that the proposed VAE outperforms classical adaptive detectors such as the Matched Filter and the Normalized Matched Filter, especially in challenging noise conditions, highlighting its robustness and adaptability in radar applications. |
| title | Out-of-Distribution Radar Detection in Compound Clutter and Thermal Noise through Variational Autoencoders |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.04861 |