This article proposes an impact time and angle control guidance law based on geometry without estimating the time-to-go. A moving virtual target is artificially placed on the desired collision line, and the missile pursues the virtual target to satisfy the desired impact angle. The moving speed of the virtual target is also dynamically adjusted to cater for the impact time constraint. We prove that the proposed geometrical rule ensures target capturability and zero terminal guidance command. Then, the standard optimal control is leveraged to develop an analytical guidance command that drives the missile to converge to the desired collision course. Numerical simulations are conducted to validate the proposed guidance law.

The Impact-Time-Angle-Control guidance (ITACG) law can control the impact time and angle in a wide range of applications. The previous ITACG research generally suffer from the accuracy under the time-varying velocity condition, or the axial acceleration is required under 3-D conditions. This paper develops a four-stage guidance technique based on the Bezier curve, and it is effective in both 2-D and 3-D scenarios. First, we proved a proposition that the length of the two-stage Bezier trajectory increases monotonically with the initial flight path angle. Based on this, the four-stage guidance strategy is studied. The first stage is to adjust the impact angle in the line-of-sight coordinate system. The second stage is to change the heading angle to match the Bezier trajectory with the distance-to-go. The third stage tracks the Bezier trajectory generated in the second stage. The fourth stage uses the proportional navigation guidance law to realize the end-game guidance. The proposed strategy was extended to 3-D scenarios, where the algorithm performed well even under the influence of gravity and aerodynamic drag, achieving accurate impact angle and time with high robustness. Finally, the effectiveness of the proposed algorithm is verified through four sets of simulation experiments.

This paper proposes a novel guidance law based on model predictive control for protecting an aircraft from a homing missile. Considering that in the target‐missile‐defender engagement the attacking missile is pursuing the target aircraft by employing a known linear guidance law, a non‐linear predictive model for the engagement is established. This predictive model allows converting the three‐body engagement to a one‐on‐one engagement. The time‐to‐go for the defender towards the future position of the incoming missile is analytically derived, indicating that the optimal time for the defender to intercept the incoming missile is a fixed‐point of a real‐valued function. As a result, the optimal interception duration can be obtained by a standard fixed‐point iteration method, and thus the interception point can be readily predicted via integrating the predictive model. The guidance command is generated by directly leading the defender to the predicted intercept point. Numerical simulations in different scenarios validate the algorithm to be computationally efficient, and demonstrate that the proposed strategy outperforms previous techniques in terms of miss distance, engagement duration, and the control effort.

To overcome the problems of traditional distributed target allocation algorithms in terms of lack of target strategic priority, poor scalability, and robustness, this paper proposes a proximal strategy optimization algorithm that combines threat assessment and attention mechanism (TAPPO). Based on the distributed training framework, the algorithm integrates a threat assessment and dynamic attention strategy and designs a dynamic reward function based on the current hit rate of the drone and the missile benefit ratio to improve the algorithm’s exploration ability and scalability. Through an 8vs8 multi-UAV confrontation experiment in a digital twin simulation environment, the results show that the agent using the TAPPO algorithm for target allocation defeats the state machine with an 85% winning rate and is significantly better than other current mainstream target allocation algorithms, verifying the effectiveness of the algorithm.