智能手机音频信号与MEMS传感器的紧耦合室内定位方法

doi:10.11947/j.AGCS.2021.20200551

测绘学报 ›› 2021, Vol. 50 ›› Issue (2): 143-152.doi: 10.11947/j.AGCS.2021.20200551

• 大地测量学与导航 • 下一篇

智能手机音频信号与MEMS传感器的紧耦合室内定位方法

陈锐志, 郭光毅, 叶锋, 钱隆, 徐诗豪, 李正

武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079

收稿日期:2020-11-17 修回日期:2021-01-16 发布日期:2021-03-03
通讯作者: 郭光毅 E-mail:guangyi.guo@whu.edu.cn
作者简介:陈锐志(1963-),男,教授,博士生导师,研究方向为室内定位、卫星导航和位置服务。E-mail:ruizhi.chen@whu.edu.cn
基金资助:
国家重点研发计划（2016YFB0502200；2016YFB0502201）；国家自然科学基金（91638203）；中国博士后科学基金（2020M682480）

Tightly-coupled integration of acoustic signal and MEMS sensors on smartphones for indoor positioning

CHEN Ruizhi, GUO Guangyi, YE Feng, QIAN Long, XU Shihao, LI Zheng

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China

Received:2020-11-17 Revised:2021-01-16 Published:2021-03-03
Supported by:
The National Key Research and Development Program of China (Nos. 2016YFB0502200;2016YFB0502201);The National Natural Science Foundation of China (No. 91638203);The China Postdoctoral Science Foundation (No. 2020M682480)

摘要/Abstract

摘要： 基于智能手机内置的传感器，开展了音频信号和MEMS传感器的紧组合室内定位研究。首先，将时频分析技术用于音频信号TOA估计，提出了一种基于短时傅里叶变换和优化互相关方法的两步估计方法。之后，研究了一种基于音频TDOA和行人航迹推算的紧组合定位算法，静态测试的平均定位精度为0.238 m，相比最小二乘方法提高了38.66%。最后，针对动态定位中存在的问题，研究了一种基于预测状态的多普勒效应补偿和异步到达时间补偿的方法，改正了TDOA估计中，由分时播发架构及行人动态移动引入的多普勒时间偏差和位置误差。动态测试的平均定位精度和方差分别为0.513 m和0.104 m²，相比于未做修正补偿的标准组合算法定位精度提高了27.64%，且测试的3款手机（华为Mate 20、OnePlus 6和Google Pixel 3）定位性能相当且无明显差异。

关键词: 室内定位, 智能手机, 音频信号, 行人航迹推算, 多源融合

Abstract: Based on the built-in sensors of a smartphone, a tightly-coupled integrated indoor positioning solution was developed based on the acoustic ranging signal and measurements from other built-in sensors. First, a two-step time of arrival (TOA) estimation method based on short-time Fourier transform and enhanced cross-correlation is performed to achieve an accurate TOA estimate. Having obtained the accurate TOA estimates, studies on a tightly-coupled integrated navigation algorithm based on TDOA and PDR is carried out. The algorithm takes the advantage of the complementarity of PDR and acoustic ranging observables to effectively improve positioning accuracy in a dynamic positioning scenario. In order to evaluate the performance of the proposed algorithm, field tests for static case and dynamic case were carried out. In the static test case, an average positioning accuracy of 0.238 m was achieved with an improvement of 38.66% compared to the least square solution purely based on acoustic ranging observables. For the dynamic test case, a method based on predicted state of Doppler compensation was applied for Doppler corrections of the TDOA observables. Furthermore, asynchronous TOA compensation, which caused by the fact that TOA measurements are not estimated in the same epoch, was also applied before feeding to the positioning algorithm. The test results demonstrated that positioning accuracy in dynamic case is 0.513 m. Compared to the solution without applying the Doppler corrections and the asynchronous TOA compensations, the performance is improved by 27.64%. Three mobile phones (Huawei Mate 20, OnePlus 6 and Google Pixel 3) were used in the field test, the performance of the positioning algorithm is consistence in all three different phones.

Key words: indoor localization, smartphone, acoustic signal, pedestrian dead reckoning, multi-source fusion

中图分类号:

P228

陈锐志, 郭光毅, 叶锋, 钱隆, 徐诗豪, 李正. 智能手机音频信号与MEMS传感器的紧耦合室内定位方法[J]. 测绘学报, 2021, 50(2): 143-152.

CHEN Ruizhi, GUO Guangyi, YE Feng, QIAN Long, XU Shihao, LI Zheng. Tightly-coupled integration of acoustic signal and MEMS sensors on smartphones for indoor positioning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(2): 143-152.

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智能手机音频信号与MEMS传感器的紧耦合室内定位方法

Tightly-coupled integration of acoustic signal and MEMS sensors on smartphones for indoor positioning

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