# Anomaly Detection in Radar Data Using PointNets

arXiv: https://arxiv.org/abs/2109.09401 · HTML: https://mlsanity.com/paper/2109.09401/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [A-CNN: Annularly Convolutional Neural Networks on Point Clouds](https://mlsanity.com/paper/1904.08017/) (2019)
- [2D Car Detection in Radar Data with PointNets](https://mlsanity.com/paper/1904.08414/) (2019)
- [Using Machine Learning to Detect Ghost Images in Automotive Radar](https://mlsanity.com/paper/2007.05280/) (2020)

## Built on by (most cited first, 4 of 4)

- [Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges](https://mlsanity.com/paper/2306.09304/) (2023)
- [Tackling Clutter in Radar Data -- Label Generation and Detection Using PointNet++](https://mlsanity.com/paper/2303.09530/) (2023)
- [Deep learning for radar data exploitation of autonomous vehicle](https://mlsanity.com/paper/2203.08038/) (2022)
- [Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data](https://mlsanity.com/paper/2311.07247/) (2023)

## Abstract (arXiv)

For autonomous driving, radar is an important sensor type. On the one hand, radar offers a direct measurement of the radial velocity of targets in the environment. On the other hand, in literature, radar sensors are known for their robustness against several kinds of adverse weather conditions. However, on the downside, radar is susceptible to ghost targets or clutter which can be caused by several different causes, e.g., reflective surfaces in the environment. Ghost targets, for instance, can result in erroneous object detections. To this end, it is desirable to identify anomalous targets as early as possible in radar data. In this work, we present an approach based on PointNets to detect anomalous radar targets. Modifying the PointNet-architecture driven by our task, we developed a novel grouping variant which contributes to a multi-form grouping module. Our method is evaluated on a real-world dataset in urban scenarios and shows promising results for the detection of anomalous radar targets.