Yu Ning, Chen Xuanhe, Feng Renjian, Wu Yinfeng
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel). 2025 May 3;25(9):2891. doi: 10.3390/s25092891.
Long-term and high-precision positioning is the key to the pedestrian indoor positioning method. The estimation methods relying only on the inertial measurement unit (IMU) itself lack external observations that can provide absolute information, and the cumulative error easily leads to the distortion of the calculated trajectory. In this paper, based on the Extended Kalman Filter (EKF) algorithm, the environmental magnetic field information is taken as the external observation quantity, and a positioning method combining inertial navigation and the magnetic field is proposed. The cumulative error is suppressed from both the yaw angle and pedestrian pose, and the overall navigation and positioning accuracy is improved. The experimental results show that the proposed fusion method greatly improves the suppression of yaw angle and displacement errors. In a total distance of 297.08 m, the yaw angle error is reduced from 11.043° to 4.778°, and the position error is reduced from 8.999 m to 0.364 m. The relative average error decreases from 3.02% to 0.12%.
长期且高精度的定位是行人室内定位方法的关键。仅依赖惯性测量单元（IMU）本身的估计方法缺乏能提供绝对信息的外部观测，累积误差容易导致计算轨迹失真。本文基于扩展卡尔曼滤波器（EKF）算法，将环境磁场信息作为外部观测量，提出一种惯性导航与磁场相结合的定位方法。从偏航角和行人姿态两方面抑制累积误差，提高了整体导航定位精度。实验结果表明，所提出的融合方法极大地改善了对偏航角和位移误差的抑制。在总距离297.08米的情况下，偏航角误差从11.043°降至4.778°，位置误差从8.999米降至0.364米。相对平均误差从3.02%降至0.12%。
