Lightweight Graph Transformers for Clutter and Target Classification in Automotive Radar
Abstract
Radar is a core sensor modality for scene perception to achieve higher levels of autonomy in automotive driving. A common occurrence in automotive radars is clutter - detections of non-existent moving objects, that can adversely impact target detection and classification performance and subsequent driving actions. We propose lightweight tiny graph transformer network (TGTNet) models for classifying clutter from stationary and moving targets in the scene. Performance evaluation on the public RadarScenes show that our proposed TGTNet models achieve similar classification performance in precision and recall metrics in comparison to state of the art models, with one to two orders of magnitude lower model size and significantly faster inference.
Fields of Study & Subjects
Radar Systems and Signal Processing
Advanced SAR Imaging Techniques
Wireless Signal Modulation Classification
Publication details
- Journal / source
- IEEE Sensors Journal
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 26
- Issue
- 6
- Pages
- 9339–9346
- Publication date
- 30 January 2026