The invention discloses a self-adaptive clustering method for unmanned aerial vehicle-mounted 4D
radar point clouds, and belongs to the field of
radar signal processing. The method comprises the steps of obtaining an original
point cloud of an unmanned aerial vehicle 4D
radar, and performing motion compensation and dynamic ROI filtering preprocessing;
dBSCAN parameter self-adaption is driven through a radar
physical model, the neighborhood
radius and the minimum core point number are dynamically adjusted, and coarse clustering is carried out on point clouds; setting a clustering quality evaluation standard, calculating a
spatial covariance matrix, a
point cloud number and geometric distribution characteristics of a coarse clustering cluster, and screening out a to-be-refined cluster with an under-segmentation risk; after a to-be-refined cluster is screened, a heterogeneous
graph model fusing space, speed and intensity features is constructed, and an RCS condition gating strategy is adopted; and carrying out recursive
cutting by utilizing
spectral clustering. According to the method, the problems of over-segmentation and under-segmentation caused by sparse point clouds along with distances and target RCS
flicker are effectively solved, and the target sensing precision and robustness of the unmanned aerial vehicle in a complex scene are improved.