TY - JOUR
T1 - 基于GRU的敌方拦截弹制导律快速辨识方法
AU - Wang, Yinhan
AU - Fan, Shipeng
AU - Wu, Guang
AU - Wang, Jiang
AU - He, Shaoming
N1 - Publisher Copyright:
© 2022, Beihang University Aerospace Knowledge Press. All right reserved.
PY - 2022/2/25
Y1 - 2022/2/25
N2 - 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.
AB - 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.
KW - Augmented proportional navigation
KW - GRU network
KW - Guidance law identification
KW - Neural network
KW - Proportional navigation
UR - http://www.scopus.com/inward/record.url?scp=85126054769&partnerID=8YFLogxK
U2 - 10.7527/S1000-6893.2021.25024
DO - 10.7527/S1000-6893.2021.25024
M3 - 文章
AN - SCOPUS:85126054769
SN - 1000-6893
VL - 43
JO - Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica
JF - Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica
IS - 2
M1 - 325024
ER -