Pedestrian navigation with handheld sensors is still particularly complex. Pedestrian Dead Reckoning method is generally used, but the estimation of the walking direction remains problematic because the device's pointing direction does not always correspond to the walking direction. To overcome this difficulty, it is possible to use gait modeling based approaches. But, these methods suffer from sporadic erroneous estimates and their accumulation over time. The HEAD (smootH Estimation of wAlking Direction) filter uses WAISS and MAGYQ angular estimates as observations to correct the walking direction and to obtain more robust and smooth results. TDCP updates are applied to constrain the walking direction estimation error while pseudo‐ranges directly update the position. HEAD is tested by 5 subjects over 21 indoor/outdoor acquisitions (between 720 m and 1.3 km). A 54% improvement is achieved thanks to the fusion in texting mode. The median obtained angular error is 5.5 in texting mode and 12 in pocket mode.

中文翻译：

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头部：通过嵌入惯性，GNSS和磁力计传感器的手持设备，可以平稳地估算行走方向

带有手持式传感器的行人导航仍然特别复杂。通常使用行人航位推测法，但是由于设备的指向方向并不总是与行走方向相对应，因此行走方向的估计仍然存在问题。为了克服这个困难，可以使用基于步态建模的方法。但是，这些方法遭受零星的错误估计及其随时间的累积。HEAD（步行方向的平滑估计）滤波器使用WAISS和MAGYQ角度估计作为观测值，以校正步行方向并获得更鲁棒和平滑的结果。TDCP更新用于限制步行方向估计误差，而伪距直接更新位置。HEAD由5位受试者在21个室内/室外采集（720 m和1.3 km之间）中进行了测试。短信模式下的融合使效果提高了54％。获得的中值角度误差为5.5在短信模式下为12，在口袋模式下为12 。