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融合蓝牙低功耗（BLE）测距与Wi-Fi指纹的室内定位图形优化模型

Graph Optimization Model Fusing BLE Ranging with Wi-Fi Fingerprint for Indoor Positioning.

作者信息

Zhou Rong, Chen Puchun, Teng Jing, Meng Fengying

机构信息

School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China.

出版信息

Sensors (Basel). 2022 May 26;22(11):4045. doi: 10.3390/s22114045.

DOI:10.3390/s22114045
PMID:35684669
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9185556/
Abstract

To improve the user's positioning accuracy of a Wi-Fi fingerprint-based positioning algorithm, this study proposes a graph optimization model based on the framework of g2o that fuses a Wi-Fi fingerprint and Bluetooth Low Energy (BLE) ranging technologies. In our model, the improvement in positioning can be formulated as a nonlinear least-squares optimization problem that a graph can represent. The graph regards users as nodes and our self-designed error functions between users as edges. In the graph, the nodes obtain the initial coordinates through Wi-Fi fingerprint positioning, and all error functions aggregate to a total error function to be solved. To improve the solution effect of the total error function and weaken the influence of measurement error, an information matrix, an edge selection principle, and a Huber kernel function are introduced. The Levenberg-Marquardt (LM) algorithm is used to solve the total error function and the affine transformation estimation is used for the drifting solution. Through experiments, the influence of the threshold in the Huber kernel function is explored, the relationship between the number of nodes in the graph and the optimization effect is analyzed, and the impact of the distribution of nodes is researched. The experimental results show improvements in the positioning accuracy of four common Wi-Fi fingerprint-matching algorithms: KNN, WKNN, GK, and Stg.

摘要

为提高基于Wi-Fi指纹的定位算法的用户定位精度，本研究提出了一种基于g2o框架的图形优化模型，该模型融合了Wi-Fi指纹和低功耗蓝牙（BLE）测距技术。在我们的模型中，定位的改进可被表述为一个图形能够表示的非线性最小二乘优化问题。该图形将用户视为节点，并将我们自行设计的用户间误差函数视为边。在图形中，节点通过Wi-Fi指纹定位获得初始坐标，所有误差函数汇总为一个待求解的总误差函数。为提高总误差函数的求解效果并减弱测量误差的影响，引入了信息矩阵、边选择原则和Huber核函数。使用Levenberg-Marquardt（LM）算法求解总误差函数，并使用仿射变换估计进行漂移求解。通过实验，探究了Huber核函数中阈值的影响，分析了图形中节点数量与优化效果之间的关系，并研究了节点分布的影响。实验结果表明，四种常见的Wi-Fi指纹匹配算法（KNN、WKNN、GK和Stg）的定位精度有所提高。

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