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An online anomaly detection method for four-dimensional tracks based on unsupervised learning

An unsupervised learning and anomaly detection technology, applied in instrumentation, design optimization/simulation, calculation, etc., can solve problems such as overfitting

Active Publication Date: 2020-08-04
BEIHANG UNIV
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Problems solved by technology

On the other hand, abnormal trajectory detection methods based on incomplete trajectory sequences often involve multiple parameter adjustments, which are prone to overfitting problems

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  • An online anomaly detection method for four-dimensional tracks based on unsupervised learning
  • An online anomaly detection method for four-dimensional tracks based on unsupervised learning
  • An online anomaly detection method for four-dimensional tracks based on unsupervised learning

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Embodiment Construction

[0068] specific implementation plan

[0069] The present invention will be further described in detail with reference to the accompanying drawings and embodiments.

[0070] Such as figure 1 As shown, the present invention discloses a four-dimensional track online anomaly detection method based on unsupervised learning, comprising the following steps: 1) establishing a four-dimensional track sequence data set according to historical flight information; 2) establishing a distance between tracks 3) Based on this distance measurement method between tracks, the historical four-dimensional track data of the selected takeoff and landing airports are segmented and clustered using the density clustering algorithm based on unsupervised learning; 4) Extract the track clusters Representative track, accurately establish the track model between each take-off and landing airport pair; 5) Define the index of gregariousness, and calculate the gregariousness of the flight in real time; 6) Defi...

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Abstract

The present invention relates to an online anomaly detection method for four-dimensional track based on unsupervised learning. Based on the distance measurement method between tracks, the historical four-dimensional track data of selected take-off and landing airports are segmented and clustered using a density clustering algorithm. And extract the representative track in the track cluster, so as to accurately establish the track model between each take-off and landing airport pair. Then, for the real-time flight, according to the distance measurement method between tracks and the track model, calculate the flight grouping degree and flight abnormal probability, judge whether the current state of the flight is abnormal according to the abnormal threshold, and update the track model in real time.

Description

technical field [0001] The invention belongs to the field of track anomaly detection, in particular to an online anomaly detection method for four-dimensional track based on unsupervised learning. Background technique [0002] With the continuous increase of air traffic demand, the air traffic management system is facing severe challenges from the flow. Realizing the real-time monitoring and abnormal identification of flights and ensuring the safety of airspace operations have become important issues of common concern in the civil and military fields. [0003] Abnormal flights refer to flights whose flight trajectory deviates from the normal trajectory. Through the real-time automatic detection and early warning of abnormal flights, it can help provide real-time rational guidance and suggestions for flight operations, thereby reducing the control pressure of air controllers. Safeguard the safe operation of the airspace traffic system. [0004] Existing trajectory anomaly d...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06K9/62
CPCG06F30/20G06F18/23
Inventor 曹先彬杜文博朱熙刘妍佟路张明远
Owner BEIHANG UNIV
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