Impact time control guidance (ITCG) is an important approach to achieve saturation attack on targets. With the increasing complexity of warfare requirements for missiles, an ITCG with field-of-view (FOV) constrained for antiship missiles is proposed based on equivalent sliding mode control. Firstly, in view of the accuracy of the calculation of remaining impact time for guidance law, the large initial lead angle is taken into consideration in the estimation of remaining flying time in which there is no need for the assumption of small angle approximation. Besides, for the sake of promoting the practical application value of the proposed guidance law, FOV is considered so that it can satisfy the actual working performance of the seeker. Then, combined with the concept of predicted interception point (PIP), the proposed guidance law is applied to attack a moving target. Numerical analysis is carried out for different initial lead angles, various impact time, different methods of estimating remaining flying time, and cooperative attack conditions. Compared with proportion navigation guidance (PNG), the feasibility and effectiveness of the guidance law are verified. Simulation results demonstrate that the proposed guidance law can guarantee the constraints of both impact time and FOV effectively.

Autonomous flight for quadrotors is maturing with the development of real-time local trajectory planning. However, the current local planning method is too conservative to waste the agility of the quadrotors. So in this paper, we have focused on aggressive local trajectory planning and proposed a gradient-based planning method to rapidly plan faster executable trajectories while ensuring it is collision-free. A distance gradient information generation strategy is proposed, which finds a collision-free Hybrid-A* path to replace the control points in obstacles for safety and creates the distance gradient used in the back-end optimization. Besides, we present a novel and aggressive time span cost term to tackle unfeasibility and improve the overall trajectory speed. Extensive simulations and real-world experiments are tested to validate our method. The results show that our proposed method generates a more aggressive trajectory with a shorter planning time and a faster flight speed than the classical gradient-based method.

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The superior performance of factor graphs compared to Kalman filtering in various fields and the use of factor graph algorithms instead of Kalman filtering algorithms in moving target localization tasks can reduce target localization error by more than 50%. However, the global factor graph algorithm may cause computational delays due to excessive computational effort. A moving target localization algorithm based on a combination of global and incremental optimization with improved factor graphs is proposed to improve localization accuracy and ensure that the computation time can be adapted to the requirements of online location. A reference point is introduced into the incremental calculation process, and it is first determined whether global or incremental calculation is used for this calculation by comparing the distance between the incremental localization results of the calculated reference point. The position of the UAV itself is then corrected by determining the position of the reference point, and this is used to finally locate the target. Simulation results show that the algorithm has good real-time performance compared to the time-consuming global algorithm. The online localization error of moving targets can be reduced by 17% compared to the incremental calculation results of the classical factor graph algorithm.