OSPA (Optimal Subpattern Assignment) distance track correlation method with fixed sliding window

Abstract

The invention relates to an OSPA (Optimal Subpattern Assignment) distance track correlation method with a fixed sliding window, which comprises the steps of: describing a local track into a set, introducing an OSPA distance evaluation between target track sets, judging whether two local tracks belong to the same track; and designing a method introduced with an upper triangular block matrix and a track correlation matrix to form a pairwise matching process between tracks of all sensors, on the basis, further designing a recursion OSPA track distance computing method with a fixed sliding window. According to the OSPA distance suggested by the OSPA distance track correlation method, same tracks can be effectively correlated, and problems of track crossing, track branching and track asynchronization can be effectively dealt. Compared with a weighting track correlation method and an independent sequential track correlation method, the OSPA distance track correlation method has special advantages on the aspects of correlation precision and track asynchronization.

Description

technical field [0001] The invention relates to a track association method with a fixed sliding window-OSPA distance, belonging to the fields of radar target tracking and pattern recognition. Background technique [0002] When multiple sensors are used for multi-target tracking, there are usually multiple tracks of a target. At this time, it is necessary to determine which tracks belong to the same target. This is the problem that track association needs to solve. Track correlation has a wide range of applications in radar network multi-target detection and tracking: First, track correlation is required to obtain the number of targets. Since the search and tracking areas of each radar are different, when the search radar obtains the initial information of the target track , start the tracking radar and start the tracking process. The network center needs to judge the number of specific targets based on the data of each search radar. Since there are overlapping areas in the r...

AI Technical Summary

Problems solved by technology

The idea of ​​track association is mainly based on the geometric distance between tracks. The association based on the information of track points at the current moment will inevitably lack the consideration of the characteristics of the entire track, and the correlation performance between tracks will inevitably decrease. , it should be combined with historical track point information as much as possible. The sequential method can combine historical information and current detection information to make judgments, effectively avoid track crossing and bifurcation, but it does encounter a big problem in dealing with track asynchronous problems

The fourth type of signal processing method generally has better accuracy, but the problem correlation is relatively strong, that is to say, it is necessary to adopt different methods and change the target motion mode according to the characteristics of the specific target track. The original established method is difficult. then apply

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