A neural network model for impact point prediction of ballistic missile based on improved second-order optimizer with parallel learning

Leliang REN; Yong XIAN; Shaopeng LI; Gang LEI; Wei WU; Bing LI

doi:10.7527/S1000-6893.2022.27964

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2023 , Vol. 44 >Issue 14: 327964 - 327964

DOI: https://doi.org/10.7527/S1000-6893.2022.27964

Electronics and Electrical Engineering and Control

A neural network model for impact point prediction of ballistic missile based on improved second-order optimizer with parallel learning

Leliang REN ,
Yong XIAN ,
Shaopeng LI ,
Gang LEI ,
Wei WU ,
Bing LI

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^1.College of War Support，Rocket Force University of Engineering，Xi’an 710025，China
^2.Department of Automation，Tsinghua University，Beijing 100084，China

E-mail： renleliang@126.com

Received date: 2022-09-01

Revised date: 2022-09-16

Accepted date: 2022-11-05

Online published: 2022-11-17

Supported by

National Natural Science Foundation of China(62103432);Young Talent fund of University Association for Science and Technology in Shaanxi(20210108)

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Abstract

To address the requirement for Impact Point Prediction （IPP） in precision guidance for the ballistic missile after high maneuver penetration， an IPP Neural Network （NN） model is proposed based on an improved second-order optimizer. The IPP of the current flight state is predicted based on the elliptical trajectory theory， and the impact deviation is decoupled. Furthermore， a sample set with flight state as input and impact deviation of elliptical trajectory as output is constructed， which greatly reduces the difficulty of NN learning. To improve the prediction accuracy， three NNs are applied to predict the three components of the impact deviation. An improved Levenberg-Marquardt optimizer for multi-GPU parallel learning is established by using the matrix block algorithm， which shortens the network learning time and reduces the demand for GPU memory. A simulation experiment is designed to analyze the advantages and computational complexity of the proposed method. Simulation results show that the proposed method has low learning difficulty， high prediction accuracy and good real-time performance. Among the 869 320 samples contained in the training set and the test set， the $3 σ$ prediction error is 4.97 m. In the learning environment with 2 GPUs， the learning time is reduced by about 49.18%. On the STM32F407 single-chip microcomputer， IPP takes 2.585 ms. The proposed method can provide support for the design of guidance algorithm， and is applicable for engineering practice.

Key words： ballistic missile; high maneuver penetration; impact point prediction; neural network; parallel learning; elliptical trajectory; single-chip microcomputer

Cite this article

Leliang REN , Yong XIAN , Shaopeng LI , Gang LEI , Wei WU , Bing LI . A neural network model for impact point prediction of ballistic missile based on improved second-order optimizer with parallel learning[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(14) : 327964 -327964 . DOI: 10.7527/S1000-6893.2022.27964

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