# A Multi-Stage Clustering Framework for Automotive Radar Data

arXiv: https://arxiv.org/abs/1907.03511 · HTML: https://mlsanity.com/paper/1907.03511/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception](https://mlsanity.com/paper/2304.06547/) (2023)
- [Deep Instance Segmentation with Automotive Radar Detection Points](https://mlsanity.com/paper/2110.01775/) (2021)
- [Full-Velocity Radar Returns by Radar-Camera Fusion](https://mlsanity.com/paper/2108.10637/) (2021)
- [CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar](https://mlsanity.com/paper/2311.01423/) (2023)
- [The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive Radar Data](https://mlsanity.com/paper/2404.01437/) (2024)
- [PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter](https://mlsanity.com/paper/2607.13891/) (2026)

## Abstract (arXiv)

Radar sensors provide a unique method for executing environmental perception tasks towards autonomous driving. Especially their capability to perform well in adverse weather conditions often makes them superior to other sensors such as cameras or lidar. Nevertheless, the high sparsity and low dimensionality of the commonly used detection data level is a major challenge for subsequent signal processing. Therefore, the data points are often merged in order to form larger entities from which more information can be gathered. The merging process is often implemented in form of a clustering algorithm. This article describes a novel approach for first filtering out static background data before applying a twostage clustering approach. The two-stage clustering follows the same paradigm as the idea for data association itself: First, clustering what is ought to belong together in a low dimensional parameter space, then, extracting additional features from the newly created clusters in order to perform a final clustering step. Parameters are optimized for filtering and both clustering steps. All techniques are assessed both individually and as a whole in order to demonstrate their effectiveness. Final results indicate clear benefits of the first two methods and also the cluster merging process under specific circumstances.