A Novel Fast Sparse Vectorized Clustering Algorithm for Range‐Spread Targets in High‐Resolution Radar

ABSTRACT

In high‐resolution radars, targets span multiple range‐Doppler (R‐D) cells, forming 2‐D range‐spread targets. Post‐CFAR detection, scattering fluctuations often fragment these targets. While standard connected component labelling efficiently processes sparse grids, its restrictive 1‐pixel adjacency fails to bridge these fractures. Grid‐based gap‐bridging incurs heavy computational burdens, and point‐cloud density algorithms suffer from an isotropic dilemma causing false adhesion or fragmentation. To address these bottlenecks, we propose a sparse vectorized clustering algorithm. By reducing the detection matrix to a sparse coordinate set and formulating an anisotropic Chebyshev tolerance, it employs a vectorized breadth‐first search to seamlessly integrate fragmented targets. Experiments on simulated and measured data demonstrate the algorithm effectively resolves the topological limitations of symmetric radii, achieving robust assignments while minimizing heuristic parameter tuning. By circumventing floating‐point operations and spatial tree searches, it exhibits strictly linear scaling, delivering computational efficiency substantially higher than conventional paradigms for real‐time radar applications.