Target Trajectory Prediction Method Based on BO-BI-LSTM under Strong Adversarial Conditions

WU Ze; TAN Mulai; DING Dali; GUO Zhengwei

doi:10.15892/j.cnki.djzdxb.2025.06.002

Journal of Projectiles, Rockets, Missiles and Guidance >

2025 , Vol. 45 >Issue 6: 978 - 985

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2025.06.002

Academic article

Target Trajectory Prediction Method Based on BO-BI-LSTM under Strong Adversarial Conditions

WU Ze ^,¹ ,
TAN Mulai ^,¹^,^* ,
DING Dali ¹ ,
GUO Zhengwei ²

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¹ Air Force Engineering University,Xi’an 710038,Shaanxi,China
² Unit 94865,Hangzhou 310000,Zhejiang, China

Received date: 2024-07-17

Online published: 2026-01-24

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Abstract

Under the current conditions of air combat confrontation,targets often behave medium and large overloads and strong maneuvers,and it is difficult to predict the trajectory of maneuver.In order to solve the problems of low prediction accuracy and short prediction time of the traditional trajectory prediction methods,a multi-step trajectory prediction method based on Bayesian optimization hyperparameters in bidirectional long short-term memory network (BO-Bi-LSTM)is proposed in this paper.The sliding prediction method is analyzed,and an online rolling prediction mathematical model is established to solve the problem of prediction value construction.The network hyperparameters are optimized automatically by using Bayesian optimization method,and the optimal hyperparameters are obtained after iteration several times.The length of the sliding window is analyzed,and the length of the sliding window with the highest prediction accuracy is obtained among the classical sliding window length.In order to test the prediction performance of this method on the maneuvering trajectory,a classical maneuvering flight trajectory is predicted and simulated in this paper,and compared with three other neural network prediction models,the simulation results prove that the bidirectional long and short time domain memory network multi-step prediction method with Bayesian optimization hyperparameters is higher in prediction accuracy than the other three neural networks.The accuracy of the 3D trajectory error is less than 200m,which can be predicted continuously for about 4.5s.

Key words： Trajectory prediction; Bi-LSTM; The online rolling recursive prediction theory; Bayesian optimization hyperparameters; Rolling optimized sliding window length

Cite this article

WU Ze , TAN Mulai , DING Dali , GUO Zhengwei . Target Trajectory Prediction Method Based on BO-BI-LSTM under Strong Adversarial Conditions[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2025 , 45(6) : 978 -985 . DOI: 10.15892/j.cnki.djzdxb.2025.06.002

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