Research on terminal maneuver strategy of anti-ship missiles

To design the terminal maneuver strategy of an anti-ship missile,first,the analytical solution of miss distance when an anti-ship missile has planar weaving maneuver and three-dimension spiral maneuver is presented,in which not only the amplitude and frequency are considered but also the initial phase is taken into account.Next,based on the analytical solution of miss distance,the effects on the miss distance of the amplitude,frequency,initial phase of the anti-ship missile＇s maneuver acceleration and the order of flight control system of the air-ship missile are analyzed.Finally,the optimum weaving maneuver and spiral maneuver which make the miss distance be the largest under some conditions are designed,which is of important meaning for increasing the survival probability of the anti-ship missile.

Maneuver strategy of lifting reentry vehicle

An optimal maneuver strategy is proposed for lifting reentry vehicle to reach the maximum lateral range after reentering the atmosphere. Aiming at problems that too many co-state variables and difficulty in estimating the initial values of co-state variables,the equilibrium glide condition (EGC) is utilized to reduce the reentry motion equations and then the optimal maneuver strategy satisfied above performance index is derived. This maneuvering strategy is applied to the lifting reentry weapon platform CAV which was designed by America recently to realize both longitudinal and lateral trajectory design by controlling the attack angle and the bank angle respectively. The simulation result indicates that the maneuver strategy proposed enables CAV to reach favorable longitudinal range and lateral range.

Combat situation suppression of multiple UAVs based on spatiotemporal cooperative path planning

Aiming at the suppression of enemy air defense(SEAD)task under the complex and complicated combat sce-nario,the spatiotemporal cooperative path planning methods are studied in this paper.The major research contents include opti-mal path points generation,path smoothing and cooperative rendezvous.In the path points generation part,the path points availability testing algorithm and the path segments availability testing algorithm are designed,on this foundation,the swarm intelligence-based path point generation algorithm is utilized to generate the optimal path.In the path smoothing part,taking ter-minal attack angle constraint and maneuverability constraint into consideration,the Dubins curve is introduced to smooth the path segments.In cooperative rendezvous part,we take esti-mated time of arrival requirement constraint and flight speed range constraint into consideration,the speed control strategy and flight path control strategy are introduced,further,the decoupling scheme of the circling maneuver and detouring maneuver is designed,in this case,the maneuver ways,maneu-ver point,maneuver times,maneuver path and flight speed are determined.Finally,the simulation experiments are conducted and the acquired results reveal that the time-space cooperation of multiple unmanned aeriel vehicles(UAVs)is effectively real-ized,in this way,the combat situation suppression against the enemy can be realized in SEAD scenarios.

Autonomous maneuver strategy of swarm air combat based on DDPG

Unmanned aerial vehicles(UAVs)have been found significantly important in the air combats,where intelligent and swarms of UAVs will be able to tackle with the tasks of high complexity and dynamics.The key to empower the UAVs with such capability is the autonomous maneuver decision making.In this paper,an autonomous maneuver strategy of UAV swarms in beyond visual range air combat based on reinforcement learning is proposed.First,based on the process of air combat and the constraints of the swarm,the motion model of UAV and the multi-to-one air combat model are established.Second,a two-stage maneuver strategy based on air combat principles is designed which include inter-vehicle collaboration and target-vehicle confrontation.Then,a swarm air combat algorithm based on deep deterministic policy gradient strategy(DDPG)is proposed for online strategy training.Finally,the effectiveness of the proposed algorithm is validated by multi-scene simulations.The results show that the algorithm is suitable for UAV swarms of different scales.