mlsanityData to 7 Oct 2026

Anomaly Detection in Radar Data Using PointNets

arXiv 2109.09401 ↗First posted 20 Sep 2021cs.CV cs.LG eess.SPcited by 4

Not on the map. Why?

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 20 Sep 2021.

Builds on

6 of 16 references.

References · 6

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  3. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  4. 2019A-CNN: Annularly Convolutional Neural Networks on Point Cloudscited by 66
  5. 20192D Car Detection in Radar Data with PointNetscited by 38
  6. 2020Using Machine Learning to Detect Ghost Images in Automotive Radarcited by 15

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3.

4 papers.

  1. 2023Radars for Autonomous Driving: A Review of Deep Learning Methods and Challengescited by 10
  2. 2023Tackling Clutter in Radar Data -- Label Generation and Detection Using PointNet++cited by 7
  3. 2022Deep learning for radar data exploitation of autonomous vehiclecited by 1
  4. 2023Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Datacited by 0