| Citation: | YANG Zicheng, XIAN Yong, LI Shaopeng, et al. Prediction method of intercept time and intercept point based on learning mid-course antimissile[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(11): 2360-2368. doi: 10.13700/j.bh.1001-5965.2020.0409(in Chinese)
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Accurately predicting the intercept point and intercept time of the interceptor in real time is an effective way to realize the mid-course penetration of ballistic missiles. In order to predict the intercept point coordinates and intercept time during the mid-course penetration process of ballistic missile, an online prediction method based on supervised learning is proposed in this paper. Using the shutdown parameters and the shutdown time of the boost stage of the interceptor as inputs, the prediction model of intercept time and intercept point was established. Based on the multi-layer perceptron neural network, a supervised learning algorithm was formulated, and the interceptor's parameters were obtained through the attack and defense simulation to make the set of training data. The network training was completed offline. The simulation results show that the neural network can effectively predict the interception time and the coordinates of interception point online, and the relative error of the prediction results is 0.124 3% and 0.128 5% respectively; the average error of the prediction results of intercept time is 0.224 0 s; the average distance error of the prediction results of intercept point is 2 016.48 m. They all meet the accuracy requirements.
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