计算机工程

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一种基于智能手机传感器的行人室内定位算法

周瑞,罗磊,李志强,桑楠   

  1. (电子科技大学 信息与软件工程学院,成都 610054)
  • 收稿日期:2015-09-10 出版日期:2016-11-15 发布日期:2016-11-15
  • 作者简介:周瑞(1974—),女,副教授、博士,主研方向为物联网、无线定位;罗磊、李志强,硕士研究生;桑楠,教授。
  • 基金项目:
    国家科技支撑计划项目(2012BAH44F00)。

An Indoor Pedestrian Positioning Algorithm Based on Smartphone Sensor

ZHOU Rui,LUO Lei,LI Zhiqiang,SANG Nan   

  1. (School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China)
  • Received:2015-09-10 Online:2016-11-15 Published:2016-11-15

摘要: 智能手机及其内置惯性传感器的普及可实现室内行人航位推算,但是由于人行走的随意性以及智能手机内置传感器精度不高,使定位精度难以满足应用要求。为此,在分析行人行走模式的基础上,基于智能手机传感器提出一种新的行人航位推算算法。对采集到的原始加速度数据进行预处理,采用基于有限状态机的行走状态转换方法识别行走周期并进行计步,利用卡尔曼滤波,结合步长-加速度关系以及连续两步步长之间的关系对步长进行估计。实验结果表明,该算法能够准确计算步数和步长,从而获得精确的室内定位结果。

关键词: 室内定位, 行人航位推算, 智能手机传感器, 卡尔曼滤波, 步数, 步长

Abstract: Advances on smartphones and built-in inertial sensors have given rise to pedestrian dead reckoning using smartphone sensors.However,an accurate Pedestrian Dead Reckoning(PDR) system using smartphone sensors is not available yet,for smartphone sensors are not accurate enough and pedestrians have natural swings during walking.Based on the analysis of pedestrian walking patterns,a new PDR algorithm using smartphone sensors is proposed.The algorithm first preproccess the original acceleration data,then uses a finite state machine to detect walking gait and thereby counts steps.Step length is estimated by using the relationship between step length and acceleration as well as that between two consecutive steps.And the estimated result is smoothed by Kalman filtering.Experimental results show that the proposed algorithm is able to provide accurate step counts and step length,thus providing accurate location service.

Key words: indoor positioning, Pedestrian Dead Reckoning(PDR), smartphone sensor, Kalman filtering, step number, step length

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