Latent-space metrics for Complex-Valued VAE out-of-distribution detection under radar clutter
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 20 references that are in the map. First posted on arXiv 25 Nov 2025.
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References · 7
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
- 2019An Introduction to Variational Autoencoderscited by 1,000
- 2017Deep Complex Networkscited by 217
- 2020Anomaly detection through latent space restoration using vector-quantized variational autoencoderscited by 45
- 2022On Out-of-Distribution Detection for Audio with Deep Nearest Neighborscited by 9
- 2025Out-of-Distribution Radar Detection in Compound Clutter and Thermal Noise through Variational Autoencoderscited by 3
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