Flight 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 low correlation accuracy, large amount of calculation, wrong association, etc., to reduce the amount of calculation and improve the search for the most The effect of excellent matching results and good global search ability
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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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