Prediction Technology of Uncontrolled Ammunition Landing Point Based on Bi-LSTM

Abstract

Landing point prediction is a commonly used algorithm in the field of guidance and control, and has high control accuracy. However, for the prediction of landing points, most algorithms do not realize end-to-end calculation, and the real-time calculation is poor. Aiming at the problem of poor real-time performance of landing calculation, a landing state prediction model based on Bi-LSTM (Bi-directional Long Short-Term Memory) neural network is proposed, and a variable step learning strategy is introduced on this basis. The model is built by taking the state of the ammunition at a certain moment (ballistic inclination, velocity, height) as the model input, and taking the landing point and landing velocity as the output of the model. And carry out simulation verification, and compare the training data fitting accuracy with the Back Propagation (BP) neural network and the LSTM neural network, as well as the complete ballistic fitting accuracy. The results show that the proposed model has a good effect in solving the problem of ammunition drop state prediction.