Fast guidance law identification approach for incoming missile based on GRU network

WANG Yinhan; FAN Shipeng; WU Guang; WANG Jiang; HE Shaoming

doi:10.7527/S1000-6893.2021.25024

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2022 , Vol. 43 >Issue 2: 325024 - 325024

DOI: https://doi.org/10.7527/S1000-6893.2021.25024

Electronics and Electrical Engineering and Control

Fast guidance law identification approach for incoming missile based on GRU network

WANG Yinhan ,
FAN Shipeng ,
WU Guang ,
WANG Jiang ,
HE Shaoming

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1. School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China;
2. Beijing Aerospace Automatic Control Institute, Beijing 100854, China

Received date: 2020-11-27

Revised date: 2020-12-21

Online published: 2021-02-02

Supported by

Beijing Municipal Science & Technology Commission Project (Z181100003218013)

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Abstract

To identify the guidance law of enemy interceptor missile, a fast guidance law identification method is proposed based on the Gate Recurrent Unit (GRU). A model for the relative motion of the interceptor missile and aircraft in the three-dimensional space is constructed, and it is assumed that the interceptor adopts the classical PN guidance law or APN guidance law. The training sample set and test sample set are extracted from the model. The input of the samples is the kinematic information of both missile and aircraft, while the label is the guidance law of the missile. A neural network with three hidden layers is established based on the GRU. The back propagation based on Adam is used to train the network. The influence of the elements including noise, sample time span, type and size of neural networks on identification accuracy is investigated by simulation, and parameters of GRU networks are optimized. Simulations are conducted under different conditions. The results show that the method proposed has better performance of anti-noise and higher precision than other types of networks. In addition, compared with the identification model based on Kalman filter, the proposed method can reduce the identification time.

Key words： guidance law identification; proportional navigation; augmented proportional navigation; GRU network; neural network

Cite this article

WANG Yinhan , FAN Shipeng , WU Guang , WANG Jiang , HE Shaoming . Fast guidance law identification approach for incoming missile based on GRU network[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022 , 43(2) : 325024 -325024 . DOI: 10.7527/S1000-6893.2021.25024

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