The Dead-reckoning Navigation Guidance Law Based on Neural Network Collaborative Forecasting

Abstract

For predicting missile’s interception point, the current guidance law based on neural networks avoids to model the strong nonlinear motion of a missile and simultaneously improve the anti-jamming ability of the guidance law. Although the advantages of solving the predicted intercept point problem based on neural networks are obvious, the difficulty in obtaining the target missile information still exists. In this work, we propose a dead-reckoning navigation guidance (DRNG) law. First, a neural network-based collaborative forecast scheme is proposed and utilize the advantages of different neural networks to greatly reduce the difficulty in acquiring the target information. Second, we construct an approximate realistic aerodynamic characteristics environment to simulate the motion parameters of missiles and targets. We also introduce real-time error correction for increasing the prediction accuracy of the network and improve the robustness of the proposed DRNG by using the model self-update algorithm. Finally, through a large number of simulation experiments, results show that the proposed DRNG completes the interception task in a noisy environment, when only the position parameters of a target missile are known. Moreover, it has a more optimized ballistic trajectory as compared with the traditional guidance law.