Multi-target tracking method based on multi-feature matching

Abstract

The invention relates to a multi-target tracking algorithm based on multi-feature matching, which comprises the steps of firstly, establishing a feature matrix of all targets in allusion to a problemthat the number of matching features is too small when a measuring point is associated with a target, wherein the feature matrix includes test statistic features and Doppler frequency features of alltargets; secondly, comparing features of all association points with the feature matrix of the target when the optimal association point is searched so as to find out an association point with the highest matching degree with the target; and finally, determining the motion state estimation value, test statistics and Doppler frequency features of the target at the current tracking moment to estimate the motion state of the target after the current tracking moment. The multi-target tracking method solves a problem of inaccurate target trajectory tracking when the trajectories of multiple targetsare intersected and overlapped through establishing the feature matrix and finding the association point which is the most matched with the feature matrix of the target.

Description

technical field [0001] The invention belongs to the technical field of radar, and further relates to a multi-target tracking method based on multi-feature matching in the technical field of radar target tracking. The invention can be used for tracking multiple target motion tracks by ground radar in complex scenes. Background technique [0002] Multi-target tracking has always been one of the important issues in the field of radar technology. In the field of radar technology, the main task of multi-target tracking technology is to detect the movement trajectories of multiple targets from the continuous time multi-frame data received by the radar receiver, and then determine the positions of these targets in each frame of data. The core parts of multi-object tracking are data association and state estimation. Due to the complexity of the actual observation scene and the target to be observed, there are many uncertain factors, which can easily lead to the blurring of multi-t...

AI Technical Summary

Problems solved by technology

This method has two disadvantages: First, when the tracks of multiple targets intersect or overlap, the measurement points of the target currently being tracked, the measurement points of other targets and the false alarm points fall into the target to be tracked at the same time. At the current moment, the probability of tracking the wave gate is greatly improved. Since this method only calculates the association probability between these measurement points and the target based on the coordinates of all measurement points in the tracking wave gate, this method only uses a single feature for association. The method has a higher probability of causing measurement points or false alarm points belonging to other targets to be associated with the current tracking target, so that the estimation of the motion state of the current tracking target and the tracking of the future trajectory will deviate.

Second, this method uses the calculation formula of membership degree to calculate the correlation probability of all effective measurement points and target trajectories. The calculation method is relatively complicated, and the calculation amount is large, which reduces the efficiency of project realization.

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