Grid-based DBSCAN for clustering extended objects in radar data

This is the authors' abstract. We don't add key points for this paper.

The online observation using high-resolution radar of a scene containing extended objects imposes new requirements on a robust and fast clustering algorithm. This paper presents an algorithm based on the most cited and common clustering algorithm: DBSCAN [1]. The algorithm is modified to deal with the non-equidistant sampling density and clutter of radar data while maintaining all its prior advantages. Furthermore, it uses varying sampling resolution to perform an optimized separation of objects at the same time it is robust against clutter. The algorithm is independent of difficult to estimate input parameters such as the number or shape of available objects. The algorithm outperforms DBSCAN in terms of speed by using the knowledge of the sampling density of the sensor (increase of app. 40–70%). The algorithm obtains an even better result than DBSCAN by including the Doppler and amplitude information (unitless distance criteria).