Real-time midcourse guidance with intercept point prediction

In this paper, an on-line suboptimal midcourse guidance law, which is a neu ral-network approximation of the optimal feedback strategy, is proposed to eliminate the need for solving two-point boundary-value problems in real ti me. Moreover, for intercept point prediction, a fast converging, iterative algorithm based on a neural network time-to-go estimator is devised. Comput er simulations confirm that the closed-loop behavior of the proposed guidan ce law is so close to the optimal trajectory that it outperforms nonoptimal guidance laws such as g-biased proportional navigation. (C) 1998 Elsevier Science Ltd. All rights reserved.