基于模糊Kalman滤波的港口车辆组合定位方法研究

Research on port vehicle combined positioning method based on fuzzy Kalman filter

针对北斗卫星导航系统(BDS)/惯性导航系统(INS)组合导航系统在遮挡环境下定位失效这一问题,通过分析组合导航系统中传感器的状态变化对定位精度的影响,设计了一种基于传感器工作状态的模糊逻辑推理系统,并与卡尔曼滤波算法相结合,通过实时调整系统量测噪声方差的方法提高定位精度.在港口环境下的无人车辆上进行了实验,实验表明,提出的方法能有效提高遮挡环境下无人车辆的定位精度,并具有良好的鲁棒性.

Aiming at the problems of BDS/INS integrated navigation system positioning failure in the obstructed environment,by analyzing the impact of sensor status changes in the integrated navigation system on positioning accuracy,we design a fuzzy logic inference system based on the status of the system,combined with the Kalman filter algorithm,the positioning accuracy is improved by real-time adjustment of the noise variance of the system measurement.Finally,experiments were carried out on unmanned vehicles in a port environment.Experiment shows that the proposed method can effectively improve the positioning accuracy of unmanned vehicles in an occluded environment,and has good robustness.