Error compensation-based group track fine association algorithm under system error

Abstract

To solve the difficult problem about fine association of tracks of various objects in a group under the system error, based on the characteristics of group tracks and in combination with an error estimation technology and a track association technology, the invention provides an error compensation-based group track fine association algorithm. The algorithm is characterized in that first, group identification is carried out on tracks obtained by various sensors based on a circulating threshold model, and overall pre-association is carried out on the group tracks based on a group center track, then, a pre-association group track most approximate to a resolution state is searched or established based on a group track state identification model, afterwards, the final error estimated value is obtained based on a group track system error model and an error confirmation model, and the error compensation is completed, and finally, fine association is carried out on the group tracks by utilizing a traditional track association algorithm. Compared with a fuzzy track alignment association algorithm based on unchanged-object information amount, a track alignment association algorithm based on track iteration and a corrected weighting method, the error compensation-based group track fine association algorithm provided by the invention has the advantages that the comprehensive performance is more excellent, and the requirements of engineering for precise association of tracks of objects in a group under the system error are well met.

Description

1. Technical field [0001] The invention belongs to the technical field of multi-sensor multi-target information fusion, and specifically relates to group track identification, error estimation and compensation, group track association, etc., and solves the problem of accurate association of target tracks within a group under system errors. 2. Background technology [0002] In the real environment, often due to factors such as uncontrollable or specific human purposes, a complex target group will be formed in a small airspace distribution range, such as the fragmentation of space debris, a large number of debris and Decoys, missiles, and aircraft formations, etc., these target airspaces are distributed in a small range, the difference in motion characteristics is not obvious, the relative motion speed is low, and the characteristics are close. The field of object tracking refers to such objects as swarm objects. In recent years, with the improvement of sensor resolution, gro...

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Problems solved by technology

[0004] However, the traditional track correlation algorithm under the system error does not estimate the complexity of the target track in the group, the design is relatively simple, and the overall correlation effect is very limited

First of all, the spatial distance of each target in the group is small and the behavior patterns are similar; if the fuzzy track correlation algorithm under the system error is used, the heading, speed and other factors concentrated in the fuzzy factors have lost their auxiliary role in the correlation decision, and continuing to use it will interfere with the correlation algorithm. Correct fuzzy judgment will increase the error correlation rate of the track; if the track alignment correlation algorithm based on complex domain topological description is used, the rough correlation gates of the track will cross seriously, and the splitting of the correlation information matrix will easily cause calculation explosion. It is difficult to meet the real-time requirements of the system; if the track alignment correlation algorithm based on overall image matching is used, the time to estimate the rotation and translation will be prolonged, and when the measurement error is large, the estimated value may diverge and cannot be realized Real-time and accurate correlation of tracks

Secondly, the similarity of each track before and after the time is very strong, and the wrong track correlation will continue to exist in the subsequent time. At this time, the traditional double threshold criterion is used to confirm the correlation pair, which will increase the wrong track correlation rate.

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