测绘通报 ›› 2024, Vol. 0 ›› Issue (4): 76-82.doi: 10.13474/j.cnki.11-2246.2024.0413

• 学术研究 • 上一篇    下一篇

一种Wi-Fi RTT/数据驱动惯性导航行人室内定位方法

周宝定1, 胡超2, 孙超3, 刘旭1, 吴鹏1, 杨钧富2   

  1. 1. 深圳大学土木与交通工程学院, 广东 深圳 518060;
    2. 深圳大学建筑与城市规划学院, 广东 深圳 518060;
    3. 中石化石油工程地球物理有限公司北斗运营服务中心, 江苏 南京 210000
  • 收稿日期:2023-07-21 发布日期:2024-04-29
  • 作者简介:周宝定(1986—),男,博士,副教授,研究方向为室内定位。E-mail:bdzhou@szu.edr.cn

A Wi-Fi RTT/data-driven inertial navigation pedestrian indoor positioning method

ZHOU Baoding1, HU Chao2, SUN Chao3, LIU Xu1, WU Peng1, YANG Junfu2   

  1. 1. School of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China;
    2. School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China;
    3. Sinopec Petroleum Engineering Geophysical Company Limited Beidou Operation Service Center, Nanjing 210000, China
  • Received:2023-07-21 Published:2024-04-29

摘要: 为了研究基于智能手机的行人室内定位方法,并提高其精度,本文提出了一种基于Wi-Fi往返时间(RTT)、惯性测量单元(IMU)的定位系统。该方法主要包括3部分:①使用扩展卡尔曼滤波融合测距信息的Wi-Fi RTT室内定位方法;②适用于多手机使用模式的航位推算方法,该方法基于长短时记忆模型(LSTM)建立神经网络模型,预测行人运动速度及航向;③基于误差状态卡尔曼滤波的Wi-Fi RTT/数据驱动惯性导航融合定位方法,进一步提高定位精度。试验结果表明,与单一的基于Wi-Fi RTT方法和数据驱动惯性导航方法相比,本文方法的平均定位精度提升了10%~20%。

关键词: 智能手机, 数据驱动惯性导航, Wi-Fi RTT, 行人航迹推算, 融合定位

Abstract: In pursuit of investigating pedestrian indoor positioning methods based on smartphones and enhancing the precision of indoor pedestrian localization, this paper proposes a localization system utilizing Wi-Fi RTT and IMU for the indoor positioning of pedestrians using smartphones. The method comprises three key components: ①The introduction of a Wi-Fi RTT indoor positioning method that employs extended Kalman filtering to integrate distance measurement information.②The proposition of a dead reckoning method suitable for multi-phone usage, utilizing LSTM to establish a neural network model for predicting pedestrian movement speed and heading.③The development of a fusion positioning method based on ESKF that combines Wi-Fi RTT and data-driven inertial navigation to further elevate positioning accuracy. Experimental findings illustrate that, in comparison to individual Wi-Fi RTT and data-driven inertial navigation methods, the proposed approach achieves an average improvement of 10% to 20% in positioning accuracy.

Key words: smartphones, data-driven inertial navigation, Wi-Fi RTT, PDR, fusion positioning

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