Abstract

According to well-described literature concerning the work history of multipath mitigation in the global navigation satellite systems (GNSS), multipath is still the most dominant factor in a challenging environment. There are unperturbed harsh circumstances where GNSS signals cannot reach and smartphone navigation is not possible. The main objective of this research is to find an accurate solution for pedestrian smartphone navigation in a multipath environment. Experiments are done with micro-electro-mechanical system (MEMS) sensors mounted on a smartphone, and no extra hardware is needed. The latest Android smartphone is used to log the data files of GNSS and MEMS sensors. This scheme has been classified in the synopsis, and a rectangular route with three perpendicular turns has been selected for a pedestrian walk. The data is preprocessed using a low pass filter to remove high-frequency noise and smooth the signal. The description of accumulative error produced by the heading and step size estimation has been reduced by implementing the indices of mean cumulative heading error and cumulative step length error, respectively. In the end, the suboptimal extended Kalman filter algorithm is used to fuse the data of GNSS and pedestrian dead reckoning (PDR) for final results. In this paper, we try to give a technique to provide accurate pedestrian smartphone navigation. The fusion results show that the prospective method explores the possibility to use smartphone navigation in any case when GNSS or PDR information is not available. Substantial simulations are implemented and corroborate that the schemed method is sturdier to use in a harsh environments. The aim is to achieve high-level accuracy with an ultra-low-cost solution.

中文翻译：

---

使用扩展的卡尔曼滤波器对行人智能手机导航进行精确且直接的GNSS / PDR集成

摘要

根据有关全球导航卫星系统（GNSS）中多径缓解工作历史的文献报道，在充满挑战的环境中，多径仍然是最主要的因素。在不受干扰的恶劣环境下，GNSS信号无法到达，无法进行智能手机导航。这项研究的主要目的是为多路径环境中的行人智能手机导航找到一种精确的解决方案。实验是通过安装在智能手机上的微机电系统（MEMS）传感器完成的，不需要额外的硬件。最新的Android智能手机用于记录GNSS和MEMS传感器的数据文件。该方案已在大纲中进行了分类，并且已选择具有三个垂直转弯的矩形路线进行人行道行走。使用低通滤波器对数据进行预处理，以去除高频噪声并使信号平滑。通过分别实现平均累积航向误差和累积步长误差的指标，减少了航向和步长估计产生的累积误差的描述。最后，使用次优扩展卡尔曼滤波算法将GNSS和行人航位推算（PDR）数据融合在一起，以获得最终结果。在本文中，我们尝试提供一种技术来提供准确的行人智能手机导航。融合结果表明，前瞻性方法探索了在无法获得GNSS或PDR信息的任何情况下使用智能手机导航的可能性。进行了大量的仿真，并证实了所设计的方法在恶劣的环境中更坚固。