# Out-of-Distribution Radar Detection in Compound Clutter and Thermal Noise through Variational Autoencoders

arXiv: https://arxiv.org/abs/2503.04861 · HTML: https://mlsanity.com/paper/2503.04861/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Anomaly detection through latent space restoration using vector-quantized variational autoencoders](https://mlsanity.com/paper/2012.06765/) (2020)
- [Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation](https://mlsanity.com/paper/2007.08128/) (2020)
- [On Out-of-Distribution Detection for Audio with Deep Nearest Neighbors](https://mlsanity.com/paper/2210.15283/) (2022)
- [MCROOD: Multi-Class Radar Out-Of-Distribution Detection](https://mlsanity.com/paper/2303.06232/) (2023)
- [CFARnet: deep learning for target detection with constant false alarm rate](https://mlsanity.com/paper/2208.02474/) (2022)

## Built on by (most cited first, 3 of 3)

- [Latent-space metrics for Complex-Valued VAE out-of-distribution detection under radar clutter](https://mlsanity.com/paper/2511.19805/) (2025)
- [Out-of-Distribution Radar Detection with Complex VAEs: Theory, Whitening, and ANMF Fusion](https://mlsanity.com/paper/2601.18677/) (2026)
- [Position-Aware Self-supervised Representation Learning for Cross-mode Radar Signal Recognition](https://mlsanity.com/paper/2602.11196/) (2026)

## Abstract (arXiv)

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.