Pedestrian navigation with daily smart devices has become a vital issue over the past few years and the accurate heading estimation plays an essential role in it. Compared to the pedestrian dead reckoning (PDR) based solutions, this article constructs a scalable error model based on the inertial navigation system and proposes an adaptive heading estimation algorithm with a novel method of relative static magnetic field detection. To mitigate the impact of magnetic fluctuation, the proposed algorithm applies a two-way Kalman filter process. Firstly, it achieves the historical states with the optimal smoothing algorithm. Secondly, it adjusts the noise parameters adaptively to reestimate current attitudes. Different from the pedestrian dead reckoning-based solution, the error model system in this article contains more state information, which means it is more sensitive and scalable. Moreover, several experiments were conducted, and the experimental results demonstrate that the proposed heading estimation algorithm obtains better performance than previous approaches and our system outperforms the PDR system in terms of flexibility and accuracy.

中文翻译：

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基于自适应卡尔曼滤波的智能手机行人导航算法

在过去几年中，使用日常智能设备进行行人导航已成为一个至关重要的问题，准确的航向估计在其中起着至关重要的作用。与基于行人航位推算（PDR）的解决方案相比，本文构建了基于惯性导航系统的可扩展误差模型，并提出了一种具有相对静磁场检测新方法的自适应航向估计算法。为了减轻磁波动的影响，所提出的算法应用了双向卡尔曼滤波过程。首先，它用最优平滑算法实现历史状态。其次，它自适应地调整噪声参数以重新估计当前姿态。与基于行人航位推算的解决方案不同，本文中的误差模型系统包含更多的状态信息，这意味着它更敏感和可扩展。此外，还进行了多次实验，实验结果表明，所提出的航向估计算法比以前的方法获得了更好的性能，并且我们的系统在灵活性和准确性方面优于 PDR 系统。