Four-dimensional track online abnormity detection method based on unsupervised learning

Abstract

The invention relates to a four-dimensional track online abnormity detection method based on unsupervised learning. On the basis of the method for measuring the distance between the tracks, the historical four-dimensional track data of the selected take-off and landing airports are subjected to segmented clustering by adopting a density clustering algorithm, and the representative tracks in trackclusters are extracted, so that the inter-pair track model of each take-off and landing airport is accurately established. For the flights flying in real time, according to an inter-flight-path distance measurement method and a flight path model, the flight grouping degree and the flight abnormality probability are calculated, whether the current state of the flights is abnormal or not is judged according to an abnormality threshold value, and the flight path model is updated 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...

AI Technical Summary

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

Images