Multi-target clustering method and device for automobile and vehicle-borne radar

Abstract

The invention discloses a multi-target clustering method and a device of an automobile and a vehicle-borne radar, wherein, the method comprises the following steps: S1, obtaining an original target array to be polled and marking each element in the array as an unaccessed state; S2, polling each element in the original target array, and obtaining a corresponding new array according to the element in the unaccessed state being polled; S3, polling each element in the new array, and obtaining the corresponding cluster array according to the element in the unaccessed state being polled; S4, pollingeach element in the cluster array, and adding the element in the unaccessed state to the corresponding new array; 5, after that polling of the clust array is completed, returning to S3, and after thepoll of the new array is completed, returning to S2; 6, after that polling of the original target array is completed, the element with the largest amplitude in each new array forms a clustering target array. This method can improve the accuracy of vehicle-borne radar target clustering, and has a good clustering effect on large targets.

Description

technical field [0001] The invention relates to the technical field of vehicle radar, in particular to a multi-target clustering method of vehicle radar, a multi-target clustering device of vehicle radar and an automobile. Background technique [0002] In related technologies, when clustering vehicle-mounted radar targets, the radar detection system is used for periodic detection to obtain the multi-dimensional parameters of the detection area, and then DBSCAN (Density-Based Spatial Clustering of Applications with Noise, density-based spatial clustering with noise) is used. The clustering method) clustering algorithm clusters the traces in each detection period to obtain several clusters, each cluster represents a vehicle radar target. The input parameters of the DBSCAN clustering algorithm include the multidimensional parameters of the point trace and a set of preset neighborhood parameters (∈, MinPts), where ∈ is the radius of the circle centered on the point trace of the ...

AI Technical Summary

Problems solved by technology

[0004] However, the above-mentioned technology is an automatic detection technology for long-term monitoring and early warning in high-risk areas under various weather conditions. Did not accumulate detected target points into a target

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