Latent-space metrics for Complex-Valued VAE out-of-distribution detection under radar clutter
This addresses radar signal processing for improved detection in complex environments, but appears incremental as it compares new metrics to existing methods.
The paper tackled the problem of out-of-distribution detection in radar clutter using complex-valued variational autoencoders, proposing metrics like reconstruction error and latent-based scores, and found that these detectors showed advantages and weaknesses compared to a classical detector on synthetic and experimental data.
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.