非视距环境下顾及杆臂补偿的UWB/IMU定位算法

UWB/IMU positioning algorithm considering lever arm compensation in non-line-of-sight environment

针对非视距(NLOS)误差和传感器杆臂影响超宽带(UWB)/惯性测量单元(IMU)组合定位精度的问题,提出了一种NLOS环境下顾及杆臂补偿的定位算法。根据UWB伪距在定位过程中的局部线性特性,设计了基于强局部加权回归法的NLOS识别与抑制算法,提高了UWB迭代定位算法的精度。利用顾及杆臂的改进抗差奇异值分解无迹卡尔曼滤波(LA-IRUKF)算法对优化后的观测数据和状态模型进行组合,实现定位精度的提升和杆臂的有效补偿。最后,采用仿真数据和实测数据对LA-IRUKF算法进行验证。仿真实验结果验证了NLOS误差识别与抑制算法的有效性以及杆臂补偿的必要性。真实场景的实验结果表明,在NLOS环境下LA-IRUKF算法不仅可以准确补偿杆臂,还能抑制NLOS误差影响。相较于抗差奇异值分解无迹滤波算法、顾及杆臂的抗差奇异值分解无迹滤波算法和改进抗差奇异值分解无迹卡尔曼滤波算法,LA-IRUKF算法的定位精度分别提高了42.4%、41.6%和28.5%,具有精度高、抗差好等优点。

The integrated positioning precision of ultra-wideband(UWB)and inertial measurement unit(IMU)is susceptible to the dual influence of non-line-of-sight(NLOS)error and sensor lever arm.To attain reliable localization results,a UWB/IMU positioning algorithm considering lever arm compensation in NLOS environment is proposed.The NLOS identification and suppression algorithm based on the robust local weighted regression method is designed by exploiting the local linear characteristics of UWB localization pseudo-range in the localization process,which improves the accuracy of the UWB iterative localization algorithm.The improved robust singular value decomposition(SVD)unscented Kalman filter(UKF)algorithm considering lever arm(LA-IRUKF)is used to combine the optimized observation data and the state model to improve the positioning accuracy and effective lever arm compensation.Finally,the LA-IRUKF algorithm is validated by using simulated and empirical data.The results of the simulation experiments demonstrate the effectiveness of the NLOS error identification and suppression algorithm as well as the necessity of the lever arm compensation.The experimental results of the real scene show that the LA-IRUKF algorithm can not only accurately compensates for the lever arm,but also limits the detrimental effects of the NLOS error in NLOS environment.Compared with the robust SVD-UKF(RUKF)algorithm,the robust SVD-UKF considering lever arm(LA-RUKF)algorithm,and the improved robust SVD-UKF(IRUKF)algorithm,the LA-IRUKF algorithm improves the positioning accuracy by 42.4%,41.6% and 28.5% respectively,which has the advantages of high accuracy and good resistance to aberration.