Radar multi-target clustering method and device

Abstract

The invention discloses a radar multi-target clustering method and device. The method comprises the following steps: S1, acquiring all the detected target points in a to-be-processed radar signal, anddividing all the acquired target points into multiple regions in advance according to spatial distribution density; and S2, respectively configuring corresponding input parameters used for clusteringfor each region obtained through division in the step S1, and respectively clustering each region by adopting a density-based clustering algorithm and according to the configured input parameters, thus obtaining a multi-target clustering result output; and the device comprises a region pre-division module and a multi-target clustering module. The method disclosed by the invention has the advantages of simple implementation method as well as high clustering accuracy, good clustering effect and high clustering speed when density distribution is non-uniform.

Description

technical field [0001] The invention relates to radar signal processing, in particular to a radar multi-target clustering method and device. Background technique [0002] For example, millimeter-wave radar plays a very important role in the process of detecting targets. After the radar passes through the detection process, it will obtain a series of target point information, including information such as position coordinates, speed, signal-to-noise ratio, etc. In the subsequent radar signal processing process, it is necessary to adopt a reasonable strategy for these detected target points Clustering is performed to cluster into correct objects. When the radar scans an object with a large reflective surface (such as a wall), it will often reflect back to multiple points, and the reflected points form a point group, that is, if there is an object with a large reflective surface in the radar detection area, More point sets will be generated, and due to the different distances...

AI Technical Summary

Problems solved by technology

The DBSCAN algorithm only needs two input parameters: the neighborhood radius (Eps) and the point threshold (MinPts), but the DBSCAN algorithm is very sensitive to the input parameters. For example, as the neighborhood radius changes, the clustering effect will have a very large difference. That is, the setting of input parameters has a great influence on the clustering effect. If the input parameters are not set properly, it will directly affect the accuracy of the clustering results.

[0004] In the process of realizing radar target clustering based on DBSCAN algorithm, DBSCAN algorithm usually uses a fixed global parameter, that is, uses fixed Neighborhood radius and point threshold, and as mentioned above, when the distribution of the target point traces scanned by the radar is uneven, if the fixed global parameters are used, the clustering effect will be poor, and the input parameter setting is too small or too large. Affect the clustering effect

DBSCAN algorithm normally completes clustering as shown in Figure 1, That is, clustering becomes three suitable target groups, but because the DBSCAN algorithm requires too much input parameters, if the input parameter Eps is not set properly, the following two situations may occur: 1. If the input parameter Eps is set relatively small, it will As a result, clustering cannot be completed due to the existence of point groups, such as Figure 2 As shown, the point group in the upper left corner of the figure fails to be clustered successfully; 2. If the input parameter Eps is set too large, it will cause redundant scattered points to be clustered into the target point group and introduce noise, such as Figure 3

[0005]Aiming at the problem of uneven distribution of the above-mentioned point set density, currently in the processing of radar target clustering, such as normalizing the distance coordinates, or introducing Such as speed, signal-to-noise ratio and other parameters form high-dimensional coordinates to calculate Euclidean distance, etc., to weaken the clustering problem caused by uneven density distribution, but this type of method is still clustering based on global input parameters, which can only It has a relatively limited weakening effect, but it still cannot solve the clustering problem with uneven distribution.

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