A Robust Correlation Method for Radar Tracks Based on Distance Stepwise Clustering

Abstract

The invention relates to the technical field of radar data fusion. Aiming at a problem of radar real-time track correlation in the complex environment of dense targets, system error time varying and inconsistent targets reported by sensors, the invention provides a radar track anti-error correlation method based on distance stepped clustering. The method comprises the steps: firstly deducing a motion platform equivalent measurement equation; secondly obtaining a state estimation decomposition equation in a global rectangular coordinate system based on a first-order Taylor expansion of the equivalent measurement; thirdly obtaining a track distance vector based on real state offset; fourthly carrying out the track rough correlation based on a distance minimum mean value; finally deducing an inter-class distance threshold value based on Gaussian random vector statistical properties, and carrying out the track fine correlation based on the distance stepped clustering. The method is mainly characterized in that the method is low in wrong correlation rate; compared with a conventional method, the method greatly improves the correlation accuracy and the adaptability to the complex environments.

Description

technical field [0001] The application belongs to the technical field of radar data fusion, and provides a radar track robust correlation method based on step-by-step distance clustering under the condition of systematic errors. Background technique [0002] In the multi-sensor information fusion system, in order to eliminate the repeated tracking of the same target by different sensors and effectively improve the tracking accuracy, it is necessary to effectively correlate the track data reported by multiple sensors, thereby eliminating redundant tracks and merging incomplete tracks. Obtain clear, unified and complete situational information of the observation environment. Due to the influence of factors such as target distribution, motion laws, and data processing methods, it is often difficult for the fusion center to make accurate correlations between the target tracks, and the existence of sensor system errors further increases the complexity of track correlation. [00...

AI Technical Summary

Problems solved by technology

Due to the influence of factors such as target distribution, movement rules, and data processing methods, it is often difficult for the fusion center to make accurate correlations between the target tracks, and the existence of sensor system errors further increases the complexity of track correlation.

[0003] The traditional track correlation method ignores the existence of systematic errors, assumes that the state estimation data of the target only contains random errors, and treats the track correlation as a global optimization problem, which causes a large false correlation rate and missing correlation in practical applications. Rate

Existing multi-sensor track robust correlation techniques, such as track robust correlation methods based on target topology information or image registration technology, have a certain degree of defects, and the accuracy of correlation, real-time calculation and adaptability to complex environments need to be improved

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