用四元数平均法提升足部导航的姿态估计精度与效率
Quaternion-Averaging-Based Adaptive Complementary Filter for Pedestrian Dead Reckoning With a Foot-Mounted AHRS
- 用马可利四元数平均融合角速度与加速度/磁力计数据
- 在不同步态阶段自适应调整传感器权重，误差更低
- 比卡尔曼滤波更省算力，适合嵌入式足部导航系统
行人死记航法（PDR）适用于室内导航系统。由于建筑物遮挡导致GPS信号衰减，而PDR可在无信号环境下估算位置。基于足部安装的惯性测量单元（AHRS）的PDR精度依赖于姿态估计算法。本文提出一种基于四元数平均的自适应互补滤波器（QAACF），以提高姿态估计精度并降低计算开销。该方法利用马可利四元数平均法，将角速度导出的四元数与加速度、磁力计导出的四元数进行融合，其融合方式比线性插值更严谨。同时，该算法根据步态阶段和磁干扰水平动态调整各传感器权重。实验结果表明，相比现有姿态估计算法，所提QAACF在保持较低计算成本的同时，实现了更低的均方根误差（RMSE），且优于卡尔曼滤波器的计算效率。
原文摘要 · Abstract (English)
Pedestrian Dead Reckoning (PDR) can be applied to indoor navigation systems. GPS suffers from signal degradation due to roofs and high-rise buildings, whereas PDR can estimate positions without being affected by such signal degradation. The accuracy of a foot-mounted AHRS(Attitude and Heading Reference System)-based PDR depends on the accuracy of the attitude estimation algorithm used in the AHRS. In this article, a Quaternion-Averaging-Based Adaptive Complementary Filter (QAACF) for PDR with a foot-mounted AHRS is proposed to improve estimation accuracy while reducing computational cost. QAACF fuses a quaternion derived from angular velocity with quaternions derived from acceleration and magnetic field measurements using Markley's quaternion averaging, which combines two quaternions more rigorously than linear interpolation. In addition, QAACF adaptively adjusts the weights of angular velocity, acceleration, and magnetic field measurements according to gait phases and the level of magnetic disturbances. Experimental results showed that the proposed QAACF achieves low Root Mean Square Errors (RMSEs) compared to existing attitude estimation filters while requiring lower computational cost than Kalman filters.
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