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基于行人航位推算和磁场匹配的智能手机室内定位

Indoor Positioning Based on Pedestrian Dead Reckoning and Magnetic Field Matching for Smartphones.

机构信息

GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China.

Collaborative Innovation Center of Geospatial Technology, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China.

出版信息

Sensors (Basel). 2018 Nov 26;18(12):4142. doi: 10.3390/s18124142.

DOI:10.3390/s18124142
PMID:30486300
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6308508/
Abstract

This paper presents an ambient magnetic field map-based matching (MM) positioning algorithm for smartphones in an indoor environment. To improve the low distinguishability of a magnetic field fingerprint at a single point, a magnetic field sequence (MFS) combined with the measured trajectory contour coming from pedestrian dead-reckoning (PDR) is used for MM. Based on the fast approximation of magnetic field gradient, a Gauss-Newton iterative (GNI) method is used to find a rigid transformation that optimally aligns the measured MFS with a reference MFS coming from the magnetic field map. Then, the position of the reference MFS is used to control the position drift error of the inertial navigation system (INS) based PDR by an extended Kalman filter (EKF) and to further improve the accuracy of the trajectory contour. Finally, we conduct several experiments to evaluate the navigation performance of the proposed MM algorithm. The test results show that the position estimation error of the MM algorithm is 0.64 m (RMS) in an office building environment, 1.87 m (RMS) in a typical lobby environment, and 2.34 m (RMS) in a shopping mall environment.

摘要

本文提出了一种基于环境磁场图的匹配（MM）定位算法，用于智能手机在室内环境中的定位。为了提高单点磁场指纹的低可分辨性，使用结合行人航位推算（PDR）测量轨迹轮廓的磁场序列（MFS）进行 MM。基于磁场梯度的快速逼近，使用高斯牛顿迭代（GNI）方法找到最佳对齐测量 MFS 与来自磁场图的参考 MFS 的刚体变换。然后，通过扩展卡尔曼滤波器（EKF）使用参考 MFS 的位置来控制基于惯性导航系统（INS）的 PDR 的位置漂移误差，并进一步提高轨迹轮廓的准确性。最后，我们进行了几次实验来评估所提出的 MM 算法的导航性能。测试结果表明，在办公楼环境中的位置估计误差为 0.64 m（RMS），在典型大厅环境中的位置估计误差为 1.87 m（RMS），在购物中心环境中的位置估计误差为 2.34 m（RMS）。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/42d298f73d25/sensors-18-04142-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/545ad6dc1488/sensors-18-04142-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/d86d412c2a75/sensors-18-04142-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/b6292063e630/sensors-18-04142-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/3b38a0f4d81e/sensors-18-04142-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/dc86a1b5297a/sensors-18-04142-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/bd850fb5c54c/sensors-18-04142-g006a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/2fca93d7de7a/sensors-18-04142-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/351fab5f71c9/sensors-18-04142-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/42d298f73d25/sensors-18-04142-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/545ad6dc1488/sensors-18-04142-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/d86d412c2a75/sensors-18-04142-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/b6292063e630/sensors-18-04142-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/3b38a0f4d81e/sensors-18-04142-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/dc86a1b5297a/sensors-18-04142-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/bd850fb5c54c/sensors-18-04142-g006a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/2fca93d7de7a/sensors-18-04142-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/351fab5f71c9/sensors-18-04142-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e7/6308508/42d298f73d25/sensors-18-04142-g009.jpg

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本文引用的文献

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