融合WiFi与可穿戴惯导模块的室内定位方法  

An indoor positioning method integrating WiFi and wearable inertial navigation module

作　　者：罗日 李燕君[1] 金志昂 陈博文 Luo Ri;Li Yanjun;Jin Zhiang;Chen Bowen(College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310000,China)

机构地区：[1]浙江工业大学计算机科学与技术学院,杭州310000

出　　处：《仪器仪表学报》2022年第3期267-276,共10页Chinese Journal of Scientific Instrument

基　　金：浙江省自然科学基金项目(LZ21F020005)资助。

摘　　要：为解决基于智能手机的人员室内定位追踪易受手机姿态影响的问题,提出一种融合WiFi与可穿戴惯导模块的室内定位方法。通过固定在胸部的惯性测量单元实现行人航迹推算PDR定位,消除手机姿态对PDR定位的影响,采用加权贝叶斯算法实现WiFi指纹定位,为PDR提供初始定位,同时基于无迹卡尔曼滤波融合WiFi定位结果与PDR定位结果,以减少PDR的累积定位误差。最后,在真实室内环境中进行大量实验,实验结果证明本文提出的加权贝叶斯WiFi定位算法相比于传统贝叶斯算法定位误差降低了51.9%,提出的融合WiFi与可穿戴惯导模块的定位方法具有更好的精度和稳定性,相比于纯PDR定位算法平均定位误差降低了65.2%,相比于完全利用手机实现的融合算法,在3种不同手机姿态下平均定位误差分别下降了12.3%、39.3%和48.4%。The smart-phone-based personnel indoor positioning is fragile to the phone attitude.To address this issue,an indoor positioning method integrating WiFi and the wearable inertial navigation module is proposed.The pedestrian dead reckoning(PDR)positioning is achieved by leveraging the wearable inertial navigation module fixed to the chest.And the influence from the smartphone attitude is avoided.WiFi fingerprint positioning is also adopted by using the proposed weighted Bayesian algorithm,which provides the initial position for PDR positioning.Meanwhile,the WiFi positioning are continuously fused with PDR positioning under the framework of the unscented Kalman filter to reduce the cumulative positioning error of pure PDR positioning.Finally,a large number of experiments are implemented in the real indoor environment.Compared with the traditional Bayesian algorithm,experimental results show that the positioning error achieved by the proposed weighted Bayesian WiFi positioning algorithm is reduced by 51.9%.The proposed positioning method integrating WiFi and the wearable inertial navigation module has better accuracy and stability.Compared with the pure PDR positioning algorithm,the average positioning error is reduced by 65.2%.Furthermore,compared with implementing the same algorithm on the smart phone,the average positioning errors under three different phone attitudes are reduced by 12.3%,39.3%and 48.4%,respectively.

关 键 词：行人航位推算 WiFi指纹定位 无迹卡尔曼滤波 可穿戴惯导模块 室内定位 

分 类 号：TN98[电子电信—信息与通信工程] TH89[机械工程—仪器科学与技术]