Intercept Point Prediction of Ballistic Missile Defense Using Neural Network Learning

Abstract

This study proposes an algorithm to calculate fast the predicted intercept point (PIP) of the anti-ballistic missile system. A neural network system is trained to learn the motion of the ballistic target to predict the future target position. Then a prediction algorithm iteratively calculates PIP and the launch time of the interceptor. PIP calculation enables the missile to effectively approach to the ballistic target. Computer simulations using simple interceptor models are conducted, demonstrating the usefulness of the proposed PIP determination algorithm. The proposed algorithm significantly reduces the computation time required for target trajectory prediction in real-time.