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.

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.

This paper proposes a land cover classification method that combines a Gaussian regression model (GRM) with an interval type-2 fuzzy neural network (IT2FNN) model as a classification decision model. Problems such as the increase in the complexity of ground cover, the increase in the heterogeneity of homogeneous regions, and the increase in the difficulty of classification due to the increase in similarity in different regions are overcome. Firstly, the local spatial information between adjacent pixels was introduced into the Gaussian model in image gray space to construct the GRM. Then, the GRM was used as the base model to construct the interval binary fuzzy membership function model and characterize the uncertainty of the classification caused by meticulous land cover data. Thirdly, the upper and lower boundaries of the membership degree of the training samples in all categories and the principle membership degree as input were used to build the IT2FNN model. Finally, in the membership space, the neighborhood relationship was processed again to further overcome the classification difficulties caused by the increased complexity of spatial information to achieve a classification decision. The classical method and proposed method were used to conduct qualitative and quantitative experiments on synthetic and real images of coastal areas, suburban areas, urban areas, and agricultural areas. Compared with the method considering only one spatial neighborhood relationship and the classical classification method without a classification decision model, for images with relatively simple spatial information, the accuracy of the interval type-2 fuzzy neural network Gaussian regression model (IT2FNN_GRM) was improved by 1.3% and 8%, respectively. For images with complex spatial information, the accuracy of the proposed method increased by 5.0% and 16%, respectively. The experimental results prove that the IT2FNN_GRM method effectively suppressed the influence of regional noise in land cover classification, with a fast running speed, high generalization ability, and high classification accuracy.

To provide a feasible solution for a variable speed unmanned aerial vehicle (UAV) to home on a target with impact time and angle constraints, this paper presents a novel geometry-based guidance law composed of trajectory reshaping and tracking. A trajectory generation process using Bezier curves is introduced to satisfy the impact time and angle constraints under time-varying speed. The impact angle is satisfied by driving the UAV along a specified ending line. The impact time is satisfied by controlling the trajectory length, which is realized through adjusting one Bezier curve end point along the ending line. The adjustable range of this end point, along with the maximum trajectory curvature, is analyzed to ensure that the trajectory is flyable. Guidance command is generated using inverse dynamics. Numerical simulations under various scenarios are demonstrated to illustrate the performance and validate the effectiveness of the proposed method.

To solve the problem regarding the impact angle of the missile, this paper proposes a novel guidance law, which can control the missile to hit the target at the desired angle. The key of the guidance law is selecting a moving point on the collision line as the virtual target, and the tactical requirements can be fulfilled by the missile directly pursuing the virtual target. The Lyapunov stable theory is used to prove the convergence of the proposed guidance law. The guidance command is generated by a PID controller to make the missile towards the virtual target. The proposed guidance law makes the lateral acceleration of the missile converge to zero, which leads the angle of attack to zero, and it theoretically guarantees the flight path angle equals the attitude angle. Numerical simulations demonstrate this impact angle control guidance law is very accurate and robust. Regardless of whether the initial heading error is large or small, the missile which employs the proposed guidance law can always hit the target from the preset direction and the guidance process is smooth.