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Next, I will briefly discuss the limitations of existing clustering approaches. In multi-beam radar systems, clustering is a critical step after detection, as it groups measurements belonging to the same physical target and directly affects tracking reliability, especially in rain and cloud clutter environments. Commonly used clustering methods include K-means, Mean Shift, OPTICS, spectral clustering, DBSCAN, and Gaussian mixture models. However, most of these methods rely on centroid-based representations to describe clustered detections. In real multi-beam radar data, this assumption often leads to several drawbacks, such as irregular elevation fluctuations over time, inverted elevation trends compared to reference trajectories, and systematic elevation overestimation. As a result, centroid-based clustering can significantly degrade 3D localization accuracy and overall tracking performance in multi-beam radar systems. |
IEEE/ICACT20260278 Slide.06
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