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Abstract This paper introduces an improved adaptive DBSCAN clustering algorithm to tackle the issues of point cloud clutter removal and sparse target classification in radar imaging systems, which are traditionally handled by signal processing methods. The proposed method constructed a Euclidean distance matrix for classification, rapidly identified central sampleswithin categories, detected and eliminated anomalous stray points, and adaptively adjusted the neighbourhood density and radius parameters for future frames based on the Euclidean distances and mutation indices of the central points. Initially, the engineering advantages of the improved algorithm were validated through simulation experiments, followed by further verification using real-world road scene data to confirm its practical effectiveness. Experimental results show that the proposed algorithm effectively eliminates clutter from target point clouds and dynamically adjusts clustering parameters to reduce sparse classification errors in targets.
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Received: 10 October 2025
Published: 11 March 2026
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2026, Vol. 9 
