Flight Trajectory Anomaly Detection Method Based on LSTM-GBSVDD Model

doi:10.12382/bgxb.2024.0489

Abstract

Abstract:

The traditional detection methods have the disadvantages of inaccurate feature extraction and low detection efficiency when processing the complex and dynamic flight trajectory data with real-time change in data length. An proposed flight trajectory anomaly detection method using the gradient-based optimization of long short-term memory network and support vector data description model based on gradient training algorithm optimization (LSTM-GBSVDD)is proposed. The LSTM network is used to extract the key features of variable-length flight trajectories and convert them into a fixed-length sequence representation. A multidimensional hypersphere classifier is constructed using the SVDD algorithm, which is used to model the normal flight trajectories and identify the potentially abnormal flight trajectories. To further improve model performance, a gradient-based training algorithm (GB) is introduced to jointly train the parameters of LSTM and SVDD, which greatly improves the detection accuracy and computational efficiency. The simulated results show that the proposed flight trajectory anomaly detection method using the gradient-based optimization of long short-term memory network and support vector data description model based on gradient training algorithm optimization (LSTM-GBSVDD) has good effectiveness and superiority in dealing with complex and changeable flight trajectory anomaly detection tasks, and has good application prospects.

Key words: flight trajectory, long short-term memory, support vector data description, anomaly detection

CLC Number:

E926

LI Lin, ZENG Yaqin, ZHU Huimin, SUN Shiyan, LIANG Weige. Flight Trajectory Anomaly Detection Method Based on LSTM-GBSVDD Model[J]. Acta Armamentarii, 2025, 46(5): 240489-.

Figures/Tables 22

Fig.1 Flowchart of flight trajectory anomaly detection method based on LSTM-GBSVDD model

Fig.2 Failt Ⅰ: aircraft speed variation graph

Fig.3 Fault Ⅱ: aircraft velocity Change Graph

Fig.4 Fault Ⅰ: aircraft altitude change graph

Fig.5 Fault Ⅱ: aircraft altitude change graph

Fig.6 Plot of angle of attack change during flight after 10th second failure of Fault Ⅰ aircraft

Fig.7 Plot of angle of attack change during flight after 10th second failure of Fault Ⅱ aircraft

Fig.8 Fault Ⅰ: aircraft side slip angle variation graph

Fig.9 Fault Ⅱ: aircraft side slip angle variation graph

Fig.10 Speed anomaly detection based on LSTM-GBSVDD model (aircraft Fault Ⅰ)

Fig.11 Speed anomaly detection based on LSTM-GBSVDD model (aircraft Fault Ⅱ)

Fig.12 High anomaly detection based on LSTM-GBSVDD model (aircraft Fault Ⅰ)

Fig.13 High anomaly detection based on LSTM-GBSVDD model (aircraft Fault Ⅱ)

Fig.14 Angle of attack anomaly detection based on LSTM-GBSVDD model (aircraft Fault Ⅰ)

Fig.15 Angle of attack anomaly detection based on LSTM-GBSVDD model (aircraft Fault Ⅱ)

Fig.16 Side slip angle anomaly detection based on LSTM-GBSVDD model (aircraft Fault Ⅰ)

Fig.17 Sideslip angle anomaly detection based on LSTM-GBSVDD model (aircraft Fault Ⅱ)

Table 1 Precision, F1, AUC score and Train Time for 200 mean simulation trials of anomaly detection of different methods in two failure modes

参数	算法	故障模式1			故障模式2
参数	算法	SVDD	LSTM-GBSVM	LSTM-GBSVDD	SVDD	LSTM-GBSVM	LSTM-GBSVDD
	Prec	0.651±0.027	0.972±0.008	0.987±0.003	0.672±0.041	0.954±0.013	0.963±0.002
速度	F1	0.534±0.031	0.901±0.013	0.913±0.001	0.577±0.032	0.906±0.010	0.925±0.001
	AUC	0.781±0.024	0.955±0.006	0.996±0.002	0.792±0.036	0.982±0.014	0.998±0.003
	训练时间/s	0.187±0.040	9.594±0.079	1.085±0.005	0.198±0.032	9.438±0.197	1.065±0.004
	Prec	0.357±0.019	0.967±0.012	0.964±0.003	0.451±0.022	0.963±0.015	0.972±0.004
高度	F1	0.467±0.026	0.924±0.006	0.927±0.001	0.437±0.046	0.908±0.017	0.923±0.001
	AUC	0.734±0.018	0.943±0.011	0.957±0.004	0.832±0.054	0.936±0.022	0.942±0.004
	训练时间/s	0.193±0.045	9.984±0.186	1.043±0.008	0.174±0.35	9.805±0.193	1.135±0.003
	Prec	0.579±0.032	0.962±0.005	0.973±0.002	0.584±0.027	0.959±0.016	0.968±0.002
攻角	F1	0.771±0.034	0.968±0.009	0.989±0.005	0.649±0.035	0.903±0.010	0.917±0.003
	AUC	0.863±0.022	0.987±0.006	0.994±0.003	0.778±0.059	0.945±0.027	0.957±0.008
	训练时间/s	0.191±0.032	9.768±0.194	1.086±0.005	0.198±0.031	9.742±0.198	1.065±0.003
	Prec	0.632±0.029	0.956±0.008	0.992±0.004	0.679±0.035	0.954±0.043	0.968±0.001
侧滑角	F1	0.576±0.017	0.919±0.007	0.925±0.002	0.543±0.026	0.960±0.035	0.973±0.004
	AUC	0.789±0.036	0.937±0.013	0.966±0.002	0.738±0.052	0.975±0.039	0.989±1.063
	训练时间/s	0.198±0.031	9.438±0.165	1.097±0.005	0.194±0.030	9.672±0.191	1.063±0.006

Fig.18 Comparison of ROC curves of flight trajectory dataset based on LSTM-GBSVDD model (aircraft Failts Ⅰ and Ⅱ)

Fig.19 Comparison of the accuracies of F1 and AUC scoring methods for detecting anomalies in Failt Ⅰ

Fig.20 Comparison of the accuracies of F1 and AUC scoring methods for detecting anomalies in Failt Ⅱ

Fig.21 Comparison of training times of anomaly detection algorithms

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