磁修正加权EKF多传感器组合导航算法研究

Research on Magnetic Modified Weighted EKF Multi-sensor Combined Navigation Algorithm

针对移动机器人在运动过程中定位导航误差不确定度高的问题,研究了基于惯性测量元件(IMU)、冗余磁导航传感器(MGS)以及射频识别传感器(RFID)相结合的移动机器人定位组合导航系统,提出了一种磁修正加权扩展卡尔曼滤波(EKF)的移动机器人混合式多传感器融合算法。该算法以降低定位导航系统的不确定度为目标,通过对多传感器提供的冗余数据进行融合,达到对移动机器人运动状态的估计。通过对冗余多传感器数据的仿真分析,磁修正加权EKF相比于传统的EKF,使滤波后的定位导航数据不确定度降低了42.3%,并且修正了系统的累计误差,验证了移动机器人冗余组合导航算法的可行性和有效性。

Aiming at the high uncertainty of positioning and navigation error of mobile robot in the process of moving,a mobile robot positioning and integrated navigation system based on inertial measurement element(IMU),redundant magnetic navigation sensor(MGS)and radio frequency identification sensor(RFID)was studied.A magnetic modified weighted extended Kalman filter(EKF)hybrid multi-sensor fusion algorithm for mobile robots was proposed.The algorithm aimed to reduce the uncertainty of the positioning and navigation system,and achieved the estimation of the motion state of the mobile robot through the fusion of redundant data provided by multiple sensors.Through the simulation analysis of redundant multi-sensor data,compared with the traditional EKF,the uncertainty of the filtered positioning and navigation data is reduced by 42.3%,and the cumulative errors of the system are corrected,which verifies the feasibility and effectiveness of the redundant integrated navigation algorithm for mobile robots.