Abstract
This paper presents a new adaptive multi-hypothesis clustering method for extended objects on radar data. The proposed method provides several clustering hypotheses per object for a given measurement set efficiently by ordering the data set similar to the HDBSCAN and extracting clusters from the ordered data set with the help of prior knowledge obtained from Extended Object Tracking (EOT) and fusion. The performance of the proposed method is tested on a manually labeled real-world data set. The dependency on accurate prior knowledge is reduced compared to previously introduced adaptive clustering methods.
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