ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2022, Vol. 59 ›› Issue (2): 452-462.doi: 10.7544/issn1000-1239.20200669

• 网络技术 • 上一篇    下一篇

基于众包的多楼层定位方法

罗娟,章翠君,王纯   

  1. (湖南大学信息科学与工程学院 长沙 410082) (juanluo@hnu.edu.cn)
  • 出版日期: 2022-02-01
  • 基金资助: 
    国家自然科学基金项目(61972140);国防基础研究计划项目(JCKY2018110C145)

Multi-Floor Location Method Based on Crowdsourcing

Luo Juan, Zhang Cuijun, Wang Chun   

  1. (College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082)
  • Online: 2022-02-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61972140) and the National Defense Basic Research Plan (JCKY2018110C145).

摘要: 无线基础设施的广泛部署使得基于WiFi的指纹定位方法成为了最具普适性的定位方法之一.然而,指纹库构建过程的耗时费力阻碍了基于接收信号强度(received signal strength indication, RSSI)指纹定位的发展.针对指纹库构建难问题,提出了一种基于众包的低成本、高效率的多楼层指纹库构建方法-MCSLoc.首先将室内平面地图转换为室内语义地图;然后采集众包用户智能手机内置惯性传感单元(inertial measurement unit, IMU)数据,采用卡尔曼滤波(Kalman filter, KF)融合算法划分传感数据到所属楼层.提出分段式轨迹获取方法,根据传感数据获取用户相对轨迹和RSSI值序列;最后利用隐马尔可夫模型(hidden Markov model, HMM)和轨迹匹配维特比(track matching Viterbi, TM-Viterbi)算法将相对轨迹与室内语义地图主路径相匹配,为RSSI值序列标注楼层标签和物理位置标签.MCSLoc方法的HMM地图匹配算法无需用户初始位置,实现众包用户弱意识参与.实验结果表明MCSLoc可以快速获取轨迹绝对初始位置,有效构建多楼层指纹库,提高多楼层定位效率.

关键词: 众包, 隐马尔可夫模型, WiFi指纹定位, 维特比算法, 地图匹配

Abstract: The widespread deployment of wireless infrastructure makes the fingerprint location method based on WiFi become one of the most universal location methods. However, the time-consuming and labor-intensive fingerprint database construction hinders the development of RSSI(received signal strength indication) fingerprint localization. In this paper, we propose a low-cost and high-efficiency multi-floor fingerprint database construction method based on crowdsourcing aiming at the difficulty of fingerprint construction. Firstly, the indoor floor plan is transformed into indoor semantic map. Secondly, the data of IMU(inertial measurement unit) in the smartphone of crowdsourcing users are collected, and the sensor data are classified into corresponding floors by KF(Kalman filter) fusion algorithm. A segmented trajectory acquisition method is proposed, according to the sensor data, the relative trajectory and RSSI value sequence of the user are acquired. Finally, HMM(hidden Markov model) and TM-Viterbi(track matching Viterbi) algorithm is used to match the trajectory with the main path of indoor semantic map, thus providing the floor label and physical location label for RSSI value sequence. The HMM map matching algorithm of MCSLoc does not need the user’s initial location. The experimental results show that MCSLoc can quickly obtain the absolute initial position of the trajectory, construct multi-floor fingerprint database effectively, and improve the efficiency of multi floor location.

Key words: crowdsourcing, hidden Markov model (HMM), WiFi fingerprint location, Viterbi algorithm, map matching

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