Multi-target tracking method based on millimeter-wave radar

Abstract

The invention discloses a multi-target tracking method based on millimeter-wave radar. The method comprises acquiring point cloud data by millimeter-wave radar; clustering the point cloud data to distinguish the echo signals of different targets; and estimating the state information of multiple observed targets according to a clustering result so as to track the multiple targets. The method improves a DBSCAN clustering algorithm according to the characteristics of the millimeter-wave radar, improves the accuracy of esimating the number of the targets and the states of the targets, and predictsand tracks the trajectories of the multiple targets in a complex environment by using a Kalman filter and data association algorithm.

Description

technical field [0001] The invention relates to the field of radar signal processing, in particular to a millimeter-wave radar multi-target tracking method. Background technique [0002] The arrival of the intelligent era will bring more and more convenience to people. As a part of intelligent life, intelligent perception will play an important role in pedestrian tracking, traffic control and other aspects by perceiving the surrounding environment at all times, understanding the status information of surrounding objects, and tracking the movement trajectory of surrounding objects. [0003] Currently commonly used sensors include visual sensors, laser radar, etc., but visual sensors cannot measure the distance information of the target. As a sensor with long detection distance, strong anti-interference ability and moderate price, wave radar will be widely used. [0004] Due to the characteristics of the millimeter-wave radar, a point cloud will be formed for the same target...

AI Technical Summary

Problems solved by technology

The traditional DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm divides the point cloud based on density, which requires that the number of points contained within a certain distance range is not less than the set number threshold, and does not need to be pre-set. Divide the number of clusters and the shape of clusters. This method can freely grow clusters of any shape in a noisy space, but this method is too dependent on distance information. When the targets meet, the point clouds of different targets are close to each other. DBSCAN The algorithm has the problem of being unable to distinguish the point clouds of different targets, and does not use the speed information of the millimeter wave radar

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