拒止环境下基于粒子滤波的无人机协同导航方法

Cooperative Navigation Method of UAV Based on Particle Filter in Denial Environment

在卫星拒止等复杂环境下的无人机主从式协同导航问题中,主机难以进行自主导航,无法满足协同导航中单机性能要求为从机提供准确的导航信息;此外,多机间的轨迹一致需保证状态一致,但多机对于同一状态信息的感知存在差异而导致多机轨迹发散.针对上述问题,首先结合偏振光定向与光流定位技术,设计融合偏振光/光流/IMU的自主导航方法,以满足多机系统中单机的性能需求,其次基于粒子滤波方法,设计相对状态一致性滤波方法改善从机相对与主机状态的一致性,从而提升多机间的轨迹一致性.最后搭建仿真与户外真机实验平台,实验结果表明:该方法可在不依赖卫星导航的情况下,相较传统的卡尔曼滤波方法位置均方根误差降低了 57%,速度误差降低了 42.5%,航向误差降低了 73%,户外实验验证了该方法可有效地提升多机间的相对状态与轨迹的一致性.

In the master-slave collaborative navigation problem of UAVs in satellite rejection environment,it is difficult for the host to perform autonomous navigation and cannot meet the performance requirements of a single machine in collaborative navigation to provide accurate navigation information for the slave and the consistency of trajectories between multiple machines needs to ensure consistent states,and differences in the perception of the same state among multiple machines can lead to divergent trajectories.To address these issues,firstly,combining polarization orientation and optical flow positioning technology,a self navigation method that integrates polarized light/optical flow/IMU is designed to meet the performance requirements of a single machine in a multi machine system.Secondly,based on the particle filter,a relative state consistency filtering method is designed to improve the consistency between the slave and the host state,so as to improve the trajectory consistency between multiple machines.Finally,a simulation and outdoor real machine experimental platform is built.The experimental results show that,compared with the traditional Kalman filtering method,the root mean square error of position is reduced by 57%,the velocity error is reduced by 42.5%,and the course error is reduced by 73%.The outdoor experiment verifies that the method can effectively improve the consistency of the relative state and trajectory between multiple aircrafts.