HEAD:用嵌入惯性、GNSS和磁力计传感器的手持设备平滑估计行走方向

Johan Perul, Valérie Renaudin
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引用次数: 2

摘要

使用手持传感器的行人导航仍然特别复杂。通常使用行人死亡计算方法,但步行方向的估计仍然存在问题,因为设备的指向方向并不总是与步行方向相对应。为了克服这一困难,可以使用基于步态建模的方法。但是,这些方法会出现零星的错误估计,并随着时间的推移而积累。HEAD(wAlking Direction的smootH估计)滤波器使用WAISS和MAGYQ角度估计作为观测值来校正行走方向,并获得更稳健和平滑的结果。TDCP更新用于约束步行方向估计误差,而伪距离直接更新位置。HEAD由5名受试者在21次室内/室外采集(720米至1.3公里之间)中进行测试。得益于短信模式的融合,实现了54%的改进。在短信模式下,获得的角度误差中值为5.5度,在口袋模式下为12度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HEAD: smootH Estimation of wAlking Direction with a handheld device embedding inertial, GNSS, and magnetometer sensors
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 degrees in texting mode and 12 degrees in pocket mode.
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