# Near out-of-distribution detection for low-resolution radar micro-Doppler signatures

arXiv: https://arxiv.org/abs/2205.07869 · HTML: https://mlsanity.com/paper/2205.07869/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 17 of 17)

- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection](https://mlsanity.com/paper/2106.09022/) (2021)
- [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](https://mlsanity.com/paper/2010.14925/) (2020)
- [A Riemannian Network for SPD Matrix Learning](https://mlsanity.com/paper/1608.04233/) (2016)
- [Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection](https://mlsanity.com/paper/2005.14140/) (2020)
- [Riemannian batch normalization for SPD neural networks](https://mlsanity.com/paper/1909.02414/) (2019)
- [geomstats: a Python Package for Riemannian Geometry in Machine Learning](https://mlsanity.com/paper/1805.08308/) (2018)
- [Unsupervised Learning of 3D Structure from Images](https://mlsanity.com/paper/1607.00662/) (2016)
- [Differentiating through the Fr'echet Mean](https://mlsanity.com/paper/2003.00335/) (2020)
- [Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification](https://mlsanity.com/paper/2001.08873/) (2020)
- [Second-order Convolutional Neural Networks](https://mlsanity.com/paper/1703.06817/) (2017)
- [From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets](https://mlsanity.com/paper/2106.11168/) (2021)
- [Deep Random Projection Outlyingness for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2106.15307/) (2021)
- [ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results](https://mlsanity.com/paper/2108.09810/) (2021)

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

- [MCROOD: Multi-Class Radar Out-Of-Distribution Detection](https://mlsanity.com/paper/2303.06232/) (2023)
- [Reconstruction-based Out-of-Distribution Detection for Short-Range FMCW Radar](https://mlsanity.com/paper/2302.14192/) (2023)
- [HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar](https://mlsanity.com/paper/2308.02396/) (2023)

## Abstract (arXiv)

Near out-of-distribution detection (OODD) aims at discriminating semantically similar data points without the supervision required for classification. This paper puts forward an OODD use case for radar targets detection extensible to other kinds of sensors and detection scenarios. We emphasize the relevance of OODD and its specific supervision requirements for the detection of a multimodal, diverse targets class among other similar radar targets and clutter in real-life critical systems. We propose a comparison of deep and non-deep OODD methods on simulated low-resolution pulse radar micro-Doppler signatures, considering both a spectral and a covariance matrix input representation. The covariance representation aims at estimating whether dedicated second-order processing is appropriate to discriminate signatures. The potential contributions of labeled anomalies in training, self-supervised learning, contrastive learning insights and innovative training losses are discussed, and the impact of training set contamination caused by mislabelling is investigated.