Cross-Scale Clustering and Neighborhood-Weighted Motion Pattern Extraction for Radar Trajectory Analysis^*

Clustering and motion pattern extraction of radar trajectories with the same flight route are of great significance for analyzing the intent of flight targets. However, the subjectivity of flight intent and the maneuverability of targets result in large differences between trajectories and increased trajectory complexity, presenting challenges for trajectory clustering and motion pattern extraction. To address these issues, this paper proposes a novel trajectory analysis algorithm for radar trajectories, capable of clustering trajectories across different spatial scales and extracting their motion patterns. Specifically, in the clustering phase, a cross-scale density clustering method is proposed to efficiently cluster trajectories with similar shapes but varying spatial scales. Subsequently, in the motion pattern extraction phase, a neighborhood-weighted algorithm is designed to extract concise and ordered motion patterns from complex trajectories with multiple loops and repetitive movements. Finally, we constructed a large-scale dataset containing 2,754 trajectories collected from real radar data throughout the entire year of 2017. Based on this dataset, we conducted a series of experiments to verify the effectiveness of the proposed algorithm. Experimental results show that our approach effectively overcomes the impact of geographical location and spatial scale differences on clustering and successfully extracts typical motion patterns from complex trajectories.