甚于LCTM的导弹拦截点预测研究

In the process of missile penetration, the prediction of interceptor interception point and interception time can provide strong support for successful penetration. Therefore, aiming at the attack and defense scenario of interceptor intercepting cruise missile, a neural network model based on LSTM is proposed for the prediction of interception time and interception point. The interceptor trajectory data is obtained through attack and defense simulation as the training set to train the network. The model takes the position of interceptor as the input, uses the neural network to estimate the interceptor trajectory type, and obtains the interception point and interception time. The experimental results show that the proposed network can effectively predict the interception point and interception time with small prediction error, and can provide an effective reference for missile penetration strategy.