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Track Matching Method Based on Genetic Algorithm

A matching method and genetic algorithm technology, applied in the field of track matching and track matching based on genetic algorithm, can solve the problems of wrong association, low accuracy of association, and large amount of calculation, so as to reduce the amount of calculation and improve the search for the most The effect of excellent matching results and good global search ability

Active Publication Date: 2022-05-17
TAIKE HIGHWAY SCI & TECH INST BEIJING CITY +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] For the track matching method in the prior art, there will be many wrong associations when there are many targets, the accuracy of the association is not high, and the calculation amount is relatively large

Method used

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  • Track Matching Method Based on Genetic Algorithm
  • Track Matching Method Based on Genetic Algorithm
  • Track Matching Method Based on Genetic Algorithm

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0030] When the target density is relatively large, the existing track matching technology will make more wrong association judgments, the accuracy of the association is not high, and the amount of calculation is large, which affects the wide use of the algorithm. For this reason, the present invention proposes a kind of track matching method based on genetic algorithm specially, see figure 1 , including the following steps:

[0031] (1) Input the track to get the associated event set:

[0032] First input the track to get the track set, and then get the track element set, use U i,j Indicates an event where two tracks match, specifically radar track X i Track Y with Surveillance System j Matching represents the same track, and the set of related events is represented by U to obtain the set of related events.

[0033] 1.1 Input track to get track set: Suppose two sets of tracks are obtained by radar and surveillance system ADS-B respectively, radar gets N track, expressed a...

Embodiment 2

[0071] The track matching method based on genetic algorithm is the same as embodiment 1, the construction fitness function described in step 3, specifically:

[0072] The fitness function is expressed as:

[0073]

[0074] Where | U k |Represents the associated event set U k The total number of events in U i,j Indicates radar track X i Track Y with Surveillance System ADS-B j For matching the same track event, the variable l i,j Indicates track X i with Y j is the probability of the same track.

[0075] In the genetic algorithm, the size of the individual fitness is used to evaluate the quality of each individual and determine the size of its genetic opportunity. The fitness function constructed by the present invention represents the track-related event set U k The correctness of the correlation event set, the more correct track correlation events in the correlation event set, the greater the value of individual fitness.

Embodiment 3

[0077] The track matching method based on genetic algorithm is the same as embodiment 1-2, and the specific steps of carrying out genetic crossover described in step (5a) are as follows:

[0078] (5a1) Find the genetic intersection point: randomly select the parent individual U in the initial population a and U b , parent individual U a The probability of the associated event is l i,j a said, U b The probability of the associated event is l i,j b Indicates that the parent individual U b The radar track most likely to be mis-matched in is i b ;Similarly, the surveillance system ADS-B track most likely to be incorrectly matched is j b . U can be calculated by the following formula b Correlation events are most likely to match incorrect tracks:

[0079]

[0080] got i b is the intersection position of the radar track, j b In order to monitor the intersection position of the system track, the same calculation method is used to obtain the parent individual U a Corr...

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Abstract

The invention discloses a track matching method based on a genetic algorithm, which solves the problems of low track matching accuracy and large amount of calculation when there are many targets, many disturbances, and many mixtures. The implementation steps are: input the radar and surveillance system ADS-B track set; form the initial population; calculate the individual fitness of the population; competitive selection; gene crossover; gene mutation; If satisfied, the optimal result will be output, otherwise a new round of selection, crossover, and mutation will be performed to finally obtain the optimal set of track matching events. The present invention imitates the selection and genetic mechanism in nature, continuously removes poorly matched tracks, and retains better matches to ensure that the optimal result is finally found. The effect of finding the optimal matching result in a limited time is used for track matching between radar and surveillance system ADS-B.

Description

technical field [0001] The invention belongs to the technical field of data fusion, and mainly relates to track matching, in particular to a track matching method based on a genetic algorithm, which can be used to compare two sets of tracks obtained by a radar with an Automatic Dependent Surveillance System-Broadcast (ADS-B) Matching, the output track with higher accuracy. Background technique [0002] Data fusion is the comprehensive processing of information from multiple sensors or sources to draw more accurate and reliable conclusions. With the development of science and technology, data fusion has become an important supporting technology for many large-scale application systems, and is widely used in military and civil fields. In the field of air traffic control, track matching is the core issue of data fusion. Using track matching to obtain accurate aircraft track is of great significance to air traffic management. [0003] In the data fusion system, each sensor has...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S13/58G01S13/88G06N3/12
CPCG06N3/126G01S13/58G01S13/88
Inventor 许录平阎博滕欣进许娜李沐青孙志峰杨升
Owner TAIKE HIGHWAY SCI & TECH INST BEIJING CITY
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