Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
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 o...
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Online Access: | https://www.mdpi.com/2079-9292/10/9/1007 |
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doaj-874479edd9ed40c890cf9ab55261af662021-04-23T23:01:54ZengMDPI AGElectronics2079-92922021-04-01101007100710.3390/electronics10091007Aircraft Track Anomaly Detection Based on MOD-Bi-LSTMYupeng Cao0Jiangwei Cao1Zhiguo Zhou2Zhiwen Liu3School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaIn 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.https://www.mdpi.com/2079-9292/10/9/1007anomaly detectionMultidimensional Outlier Descriptor (MOD)Bi-directional Long-Short Time Memory (Bi-LSTM)track outlierstrack deviation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yupeng Cao Jiangwei Cao Zhiguo Zhou Zhiwen Liu |
spellingShingle |
Yupeng Cao Jiangwei Cao Zhiguo Zhou Zhiwen Liu Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM Electronics anomaly detection Multidimensional Outlier Descriptor (MOD) Bi-directional Long-Short Time Memory (Bi-LSTM) track outliers track deviation |
author_facet |
Yupeng Cao Jiangwei Cao Zhiguo Zhou Zhiwen Liu |
author_sort |
Yupeng Cao |
title |
Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM |
title_short |
Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM |
title_full |
Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM |
title_fullStr |
Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM |
title_full_unstemmed |
Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM |
title_sort |
aircraft track anomaly detection based on mod-bi-lstm |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2021-04-01 |
description |
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. |
topic |
anomaly detection Multidimensional Outlier Descriptor (MOD) Bi-directional Long-Short Time Memory (Bi-LSTM) track outliers track deviation |
url |
https://www.mdpi.com/2079-9292/10/9/1007 |
work_keys_str_mv |
AT yupengcao aircrafttrackanomalydetectionbasedonmodbilstm AT jiangweicao aircrafttrackanomalydetectionbasedonmodbilstm AT zhiguozhou aircrafttrackanomalydetectionbasedonmodbilstm AT zhiwenliu aircrafttrackanomalydetectionbasedonmodbilstm |
_version_ |
1721512186224836608 |