基于多维模糊映射AP优化的WLAN室内定位方法

doi:10.12263/DZXB.20201457

电子学报 ›› 2022, Vol. 50 ›› Issue (8): 1875-1884.DOI: 10.12263/DZXB.20201457

基于多维模糊映射AP优化的WLAN室内定位方法

杨小龙¹^,², 李欣玥¹^,², 周牧¹^,², 王勇¹^,², 何维¹^,²

^1.重庆邮电大学通信与信息工程学院，重庆 400065
^2.重庆邮电大学移动通信技术重庆市重点实验室，重庆 400065

收稿日期:2020-12-17 修回日期:2021-04-14 出版日期:2022-08-25
- 通讯作者:
- 周牧
- 作者简介:
- 杨小龙男，1987年生于四川.重庆邮电大学信息与通信工程学院讲师，硕士生导师.主要研究方向为量子定位技术、无线感知与定位技术、认知无线电技术等.E-mail: yangxiaolong@cqupt.edu.cn
  李欣玥女，1994年生于重庆.重庆邮电大学信息与通信工程专业硕士研究生.主要研究方向为室内无线定位和模糊数学理论.E-mail: xinyue_cqupt@163.com
  周牧男，1984年生于四川.重庆邮电大学研究生院副院长、教授、博士生导师.主要研究方向为无线定位与感知技术、多源信息融合与机器学习、量子精密测量与成像技术等.E-mail: zhoumu@cqupt.edu.cn
  王勇男，1987年生于云南.重庆邮电大学通信与信息工程学院讲师，硕士生导师.主要研究方向包括毫米波感知、量子感知、深度学习与无线资源管理.E-mail: yongwang@cqupt.edu.cn
  何维女，1995年生于四川.2017年获得重庆邮电大学获学士学位.重庆邮电大学博士研究生.研究兴方向包括无线定位、量子雷达、手势识别和机器学习.E-mail: s180101119@stu.cqupt.edu.cn
- 基金资助:
- 国家自然科学基金 (61901076); 重庆市自然科学基金 (cstc2020jcyj-msxmX0842); 重庆市教委科学技术研究项目 (KJZD-K202000605)

Multi-Dimensional Fuzzy Mapping for AP Optimization Based WLAN Indoor Localization

YANG Xiao-long¹^,², LI Xin-yue¹^,², ZHOU Mu¹^,², WANG Yong¹^,², HE Wei¹^,²

^1.School of Communication and Information Engineering，Chongqing University of Posts and Telecommunications，Chongqing 400065，China
^2.Chongqing Key Laboratory of Mobile Communications Technology，Chongqing University of Posts and Telecommunications，Chongqing 400065，China

Received:2020-12-17 Revised:2021-04-14 Online:2022-08-25 Published:2022-09-08
- Corresponding author:
- ZHOU Mu

摘要/Abstract

摘要：

室内定位技术在多领域有着重要的应用，而传统的无线局域网（Wireless Local Area Network，WLAN）指纹定位方法通常很少考虑WLAN接收信号强度（Received Signal Strength，RSS）特征的多样性以及来自不同接入点（Access Point，AP）的RSS特征位置分辨力的差异性问题，从而导致WLAN定位精度不高且定位效率较低.对此，本文提出一种基于多维模糊映射AP优化的WLAN室内定位方法.在离线阶段通过多次采集RSS数据提取多维RSS特征，计算AP信息增益比及相应的离线模糊隶属度，并利用模糊关系方程求解多维RSS特征模糊权重；而在在线阶段，则通过多维模糊映射构造模糊判定矩阵并计算AP在线模糊隶属度，同时结合K近邻（K-Nearest Neighbor，KNN）算法完成对目标的位置坐标计算.实验结果表明，相较于传统的AP优化定位方法，所提方法在线阶段的定位计算开销最高减少了4.12 s，定位误差4 m内的置信概率为91.91%.

关键词: WLAN室内定位, AP优化, 多维模糊映射, 信息增益比, 模糊隶属度

Abstract:

The indoor localization technology has important applications in many fields, while traditional wireless local area network(WLAN) fingerprint-based localization methods usually rarely consider both the diversity of WLAN received signal strength(RSS) features and the difference of the position resolution of RSS features from different access points(APs), which results in the low localization accuracy and efficiency. To address this problem, this paper proposes a WLAN indoor localization method based on the multi-dimensional fuzzy mapping for the AP optimization. Specifically, in the offline phase, the information gain ratio of the AP and the corresponding offline fuzzy membership degree are calculated according to the multi-dimensional RSS features which are extracted many times, and meanwhile the fuzzy relationship equation is utilized to solve out fuzzy weights of multi-dimensional RSS features. In the online phase, the fuzzy decision matrix is constructed by the multi-dimensional fuzzy mapping to calculate the online fuzzy membership degree of the AP, and then the target location estimation is realized by combining with the K-nearest neighbor(KNN) algorithm. Experimental results show that compared with the traditional AP optimization based localization methods, the localization calculation overhead in the online stage of the proposed method is reduced by up to 4.12 s, and the confidence probability of the positioning error within 4 meters is 91.91%.

Key words: WLAN indoor localization, access point optimization, multi-dimensional fuzzy mapping, information gain ratio, fuzzy membership degree

中图分类号:

TN929.5

杨小龙, 李欣玥, 周牧, 王勇, 何维. 基于多维模糊映射AP优化的WLAN室内定位方法[J]. 电子学报, 2022, 50(8): 1875-1884.

YANG Xiao-long, LI Xin-yue, ZHOU Mu, WANG Yong, HE Wei. Multi-Dimensional Fuzzy Mapping for AP Optimization Based WLAN Indoor Localization[J]. Acta Electronica Sinica, 2022, 50(8): 1875-1884.

图/表 10

图1 系统流程图

图2 实验环境结构图

图3 选择具有不同在线模糊隶属度的AP用于定位的平均定位误差

图4 不同AP个数与λ值条件下的平均定位误差

图5 不同λ值条件下的平均定位误差

图6 不同λ值条件下的定位计算开销

图7 不同定位方法的定位计算开销

图8 选择不同AP用于定位的平均定位误差分位图

图9 不同定位方法的误差CDF

图10 AP优化前后不同定位方法的平均定位误差

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基于多维模糊映射AP优化的WLAN室内定位方法

Multi-Dimensional Fuzzy Mapping for AP Optimization Based WLAN Indoor Localization

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