Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM

Yupeng Cao,Zhiwen Liu,Zhiguo Zhou,Jiangwei Cao

doi:10.3390/electronics10091007

Abstract

In order to ensure flight safety and eliminate hidden dangers, it is very important to detect aircraft track anomalies, which include track deviations and track outliers. Many existing track anomaly detection methods cannot make full use of multidimensional information of the relevant track. Based on this problem, an aircraft track anomaly detection method based on the combination of the Multidimensional Outlier Descriptor (MOD) and the Bi-directional Long-Short Time Memory network (Bi-LSTM) is proposed in this paper. Firstly, track deviation detection is transformed into the track density classification problem, and then a multidimensional outlier descriptor is designed to detect track deviation. Secondly, track outliers detection is transformed into a prediction problem, and then a Bi-LSTM model is designed to detect track outliers. Experimental results based on real aircraft track data indicate that the accuracy of the proposed method is 96% and the recall rate is 97.36%. It can detect both track deviation and track outliers effectively.

Highlights

With the rapid development of satellite communication technology, the ADS-B data reflecting aircraft track is becoming more abundant, and there is a lot of valuable information from the track data
Latitude and height for example, ure 4 shows the track deviation detection result based on Multidimensional Outlier Descriptor (MOD), in which the red dotted
4 shows track deviation based on MOD, in which the red line indicates the the track deviated fromdetection the routeresult and the green solid line indicates the dotted norline indicates track deviated from line indicates the mal track

Summary

Introduction

With the rapid development of satellite communication technology, the ADS-B data reflecting aircraft track is becoming more abundant, and there is a lot of valuable information from the track data. There are many unreasonable sampling points that have huge differences with their neighboring track points in motion features in the track data. These points are outliers in the track data [9]. Because track outliers do not occur in isolation, the forward and backward structure of the Bi-LSTM model is more suitable for track outliers detection.

Methods

Results

Conclusion