The influence of a disturbing gravity field on the impact points of long-range vehicles (LRVs) has become increasingly prominent, which is an important factor affecting the accuracy of impact point prediction (IPP). To achieve high-accuracy and fast IPP for LRVs under the influence of a disturbing gravity field, a data-driven multi-level IPP method is proposed to balance the prediction accuracy and real-time performance. At the first level, the impact point of the current flight state is predicted based on elliptical trajectory theory, and the impact deviation of the elliptical trajectory (ID-ET) is calculated. At the second and third levels, a neural network (NN) model is established to learn the ID-ET caused by the J₂ term and re-entry aerodynamic drag as well as that caused by the disturbing gravity field. To improve the NN prediction performance, an auxiliary circle is applied to decouple the ID-ET. To reduce the difficulty of NN learning, a training strategy is designed based on the idea of curriculum learning, which improves training accuracy. At the same time, a hybrid sample generation strategy is proposed to improve the NN generalization ability. A detailed simulation experiment is designed to analyze the advantages and computational complexity of the proposed method. The simulation results showed that the proposed model has a high prediction accuracy, strong generalization ability, and good real-time performance under the influence of the disturbing gravity field and re-entry aerodynamic drag. Among the 317,360 samples contained in the training and test sets, the 3σ prediction error was 6.21 m. On an STM32F407 single-chip microcomputer, the IPP required 3.415 ms. The proposed method can provide support for the design of guidance algorithms and is applicable to engineering practice.

The advancement of maneuver penetration technology necessitates an improvement in the design of interception guidance laws to a higher level. An echelon intelligent guidance framework of “terminal guidance law model based on imitation learning (IL) → terminal guidance law evolution model based on reinforcement learning” is proposed to increase the interception probability, decrease the energy consumption, and improve the robustness of the guidance law for intercepting maneuvering targets. Firstly, a three-dimensional uncertain confrontation model between the maneuvering target and interceptor is established based on the interception collision triangle. Secondly, the IL method is used to mine the proportional navigation guidance (PNG) law, which provides a good initial policy for the subsequent reinforcement learning guidance law. Finally, a Markov decision model is established, and a process reward of energy consumption and a “soft” terminal reward model, including a “transition section,” are proposed. The proximal policy optimization (PPO) algorithm is used to fully explore the high-performance interception strategy. The findings of the Monte Carlo simulation show that the new guidance law is very stable and resilient, outperforming the conventional guidance algorithm in terms of interception probability and energy usage. Additionally, the single decision time is only 0.32 ms, making it of certain engineering value.