基于运动状态估计的双无人机协同航路规划

在双无人机协同任务过程中,由于会受到敌方雷达、武器等干扰造成跟随机无法准确获得领航机状态,为保证跟随机能够尽可能跟随领航机,提出结合交互式多模型(IMM)算法和自适应差分进化(JADE)算法的方法进行跟踪与寻迹.首先,基于JADE算法为领航机优化一条平滑航路并建立领航机位置状态信息,同时利用多个机动模型匹配估计领航机运动状态;然后,根据估计信息,确定跟随机下一可行航迹点范围;最后,以双机时空协同为目标,设计跟随机优化目标函数,优化产生下一航迹点.仿真实验表明,仅8.8%的航迹点处于双机探测范围外,且短时间内能够回到理想位置,时间和空间上双机能够保持协同.

Dual UAV collaborative route planning based on motion state estimation

When the two UAVs perform the mission together, the follower cannot fully obtain the state of the leader due to interference from enemy radars and weapons. In order to ensure that the follower can follow the leader as much as possible. This paper proposes a method of tracking and tracing combined with IMM algorithm and adaptive differential evolution (JADE) algorithm. First, the JADE algorithm is used to optimize a smooth route for the leader. At the same time, the motion state of the leader is estimated by using multiple maneuver models. Secondly, according to the estimated information, determine the following track point generation range of the follower. Finally, design the follower fitness function and optimize to generate the next track point. Experiments show that only 8.8% of the track points are outside the range of detection, and can return to the ideal position in a short time, and the two UAVs can maintain coordination in time and space.