基于信号分布混合假设检验的Wi-Fi室内定位方法

周牧; 耿小龙; 谢良波; 田增山; 卫亚聪

doi:10.11999/JEIT180147

基于信号分布混合假设检验的Wi-Fi室内定位方法

doi: 10.11999/JEIT180147

重庆邮电大学移动通信技术重庆市重点实验室重庆 400065

基金项目: 国家自然科学基金(61771083, 61704015)，长江学者和创新团队发展计划基金(IRT1299)，重庆市科委重点实验室专项经费基金，重庆市基础与前沿研究计划基金(cstc2017jcyjAX0380, cstc2015jcyjBX0065)，重庆市高校优秀成果转化基金(KJZH17117)，重庆市研究生科研创新项目(CYS17221)，重庆市教委科学技术研究项目(KJ1704083)

详细信息

作者简介:
周牧：男，1984年生，教授，研究方向为无线定位与导航技术、信号侦察与检测技术、凸优化与深度学习理论等

耿小龙：男，1993年生，硕士，研究方向为无线定位技术

谢良波：男，1986年生，讲师，研究方向为低功耗模拟/数字集成电路、低功耗SAR ADC、室内定位技术等

田增山：男，1968年生，教授，博士生导师，研究方向为移动通信、个人通信、GPS及蜂窝网定位技术等

卫亚聪：女，1993年生，硕士，研究方向为无线定位技术、数值计算等

通讯作者:
耿小龙　 343097884@qq.com

¹⁾ 对于小样基本情况，u′将近似服从Mann-Whitney分布。此时，利用Mann-Whitney U临界值表可得相应的决策值p。
中图分类号: TN961
计量
- 文章访问数: 1724
- HTML全文浏览量: 803
- PDF下载量: 133
- 被引次数: 0
出版历程
- 收稿日期: 2018-02-05
- 修回日期: 2018-06-29
- 网络出版日期: 2018-08-14
- 刊出日期: 2018-12-01

Wi-Fi Indoor Localization Based on Hybrid Hypothesis Test of Signal Distribution

Chongqing Key Laboratory of Mobile Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61771083, 61704015), The Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), The Special Fund of Chongqing Key Laboratory (CSTC), The Fundamental and Frontier Research Project of Chongqing (cstc2017jcyjAX0380, cstc2015jcyjBX0065), The University Outstanding Achievement Transformation Project of Chongqing (KJZH17117), The Postgraduate Scientific Research and Innovation Project of Chongqing (CYS17221), The Scientific and Technological Research Foundation of Chongqing Municipal Education Commission (KJ1704083)

摘要

摘要: Wi-Fi室内定位技术是目前移动计算领域的研究热点之一，而传统位置指纹定位方法没有考虑复杂室内环境下Wi-Fi信号分布的多样性问题，从而导致Wi-Fi室内定位系统的鲁棒性较差。为了解决这一问题，该文提出一种基于信号分布混合假设检验的Wi-Fi室内定位方法。首先根据Jarque-Bera(JB)检验结果对各个参考点处的Wi-Fi信号分布进行正态性评价；然后针对不同Wi-Fi信号分布特性，利用混合Mann-Whitney U检验/T检验方法构造匹配参考点集合，以实现对目标的区域定位；最后通过计算定位区域中匹配参考点的K近邻(K-Nearest Neighbor, KNN)，完成对目标的位置坐标估计。实验结果表明，所提方法相比于传统Wi-Fi室内定位方法具有更高的定位精度和更强的系统鲁棒性。
- 室内定位 /
- Jarque-Bera检验 /
- Mann-Whitney U检验 /
- T检验 /
- 信号统计分布
Abstract: Wi-Fi indoor localization technique is one of the current research hotspots in the field of mobile computing, however, the conventional location fingerprinting based localization scheme does not consider the diversity of Wi-Fi signal distribution in the complicated indoor environment, resulting in the low robustness of indoor localization system. To address this problem, a new hybrid hypothesis test of signal distribution for Wi-Fi indoor localization is proposed. Specifically, the Jarque-Bera (JB) test is conducted to examine the normality of Wi-Fi signal distribution at each Reference Point (RP). Then, according to the different Wi-Fi signal distributions, the hybrid Mann-Whitney U test and T test approaches are used to construct the set of matching reference points with the purpose of realizing the area localization. Finally, by calculating the K-Nearest Neighbor (KNN) of matching reference points in the located area, the location coordinate of the target is obtained. The experimental results indicate that the proposed approach is featured with higher localization accuracy as well as stronger system robustness compared with the conventional Wi-Fi indoor localization approaches.
- Indoor localization /
- Jarque-Bera test /
- Mann-Whitney U test /
- T-test /
- Signal statistical distribution
¹⁾ 对于小样基本情况，u′将近似服从Mann-Whitney分布。此时，利用Mann-Whitney U临界值表可得相应的决策值p。

HTML全文

图 1 实验环境结构图

下载: 全尺寸图片幻灯片

图 2 各个参考点处信号分布正态性JB检验结果

下载: 全尺寸图片幻灯片

图 3 不同区域中测试点所对应的区域匹配概率

下载: 全尺寸图片幻灯片

图 5 不同方法的误差累积概率分布比较

下载: 全尺寸图片幻灯片

图 4 不同方法的平均定位误差比较

下载: 全尺寸图片幻灯片

参考文献(18)

EL-KAFRAWY K, YOUSSEF M, EL-KEYI A, et al. Propagation modeling for accurate indoor WLAN RSS-based localization[C]. IEEE Vehicular Technology Conference Fall, Ottawa, Canada, 2010: 1–5.

YIN Feng, ZHAO Yuxin, GUNNARSSON F, et al. Received-signal-strength threshold optimization using Gaussian processes[J]. IEEE Transactions on Signal Processing, 2017, 65(8): 2164–2177 doi: 10.1109/TSP.2017.2655480

GOMEZ A, SHI K, QUINTANA C, et al. A 50 Gb/s transparent indoor optical wireless communications link with an integrated localization and tracking system[J]. Journal of Lightwave Technology, 2016, 34(10): 2510–2517 doi: 10.1109/JLT.2016.2542158

HONG K, LEE S K, and LEE K. Performance improvement in ZigBee-based home networks with coexisting WLANs[J]. Pervasive and Mobile Computing, 2015, 19(5): 156–166 doi: 10.1016/j.pmcj.2014.03.002

MAALEK R and SADEGHPOUR F. Accuracy assessment of ultra-wide band technology in locating dynamic resources in indoor scenarios[J]. Automation in Construction, 2016, 63(3): 12–26 doi: 10.1016/j.autcon.2015.11.009

刘洺辛, 孙建利. 基于能效的WLAN室内定位系统模型设计与实现[J]. 仪器仪表学报, 2014, 35(5): 1169–1178 doi: 10.19650/j.cnki.cjsi.2014.05.029

LIU Mingxin and SUN Jianli. Design and implementation of WLAN indoor positioning system model based on energy efficiency[J]. Chinese Journal of Scientific Instrument, 2014, 35(5): 1169–1178 doi: 10.19650/j.cnki.cjsi.2014.05.029

吴楠, 王旭东, 胡晴晴. 基于多LED的高精度室内可见光定位方法[J]. 电子与信息学报, 2015, 37(3): 727–732 doi: 10.11999/JEIT140725

WU Nan, WANG Xudong, and HU Qingqing. Multiple LED based high accuracy indoor visible light positioning scheme[J].Journal of Electronics&Information Technology, 2015, 37(3): 727–732 doi: 10.11999/JEIT140725

ALTINTAS B and SERIF T. Indoor location detection with a RSS-based short term memory technique (KNN-STM)[C]. IEEE International Conference on Pervasive Computing and Communications, Lugano, Switzerland, 2012: 794–798.

VELDE S V D, ARORA G, VALLOZZI L, et al. Cooperative hybrid localization using Gaussian processes and belief propagation[C]. IEEE International Conference on Communication, London, United Kingdom, 2015: 785–790.

KOVALEV M. Indoor positioning of mobile devices by combined Wi-Fi and GPS signals[C]. International Conference on Indoor Positioning and Indoor Navigation, Busan, Republic of Korea, 2015: 332–339.

CAI Sheng, LIAO Weixian, LUO Changqing, et al. CRIL: An efficient online adaptive indoor localization system[J]. IEEE Transactions on Vehicular Technology, 2017, 66(5): 4148–4160 doi: 10.1109/TVT.2016.2597303

BAHL P and PADMANABHAN V N. RADAR: An in-building RF-based user location and tracking system[C]. IEEE International Conference on Computer Communications, Tel Aviv, Israel, 2000: 775–784.

ZHOU Mu, WEI Yacong, TIAN Zengshan, et al. Achieving cost-efficient indoor fingerprint localization on WLAN platform: a hypothetical test approach[J]. IEEE Access, 2017, 5(8): 15865–15874 doi: 10.1109/ACCESS.2017.2737651

JARQUE C M and BERA A K. A test for normality of observations and regression residuals[J]. International Statistical Review, 1987, 55(2): 163–172 doi: 10.2307/1403192

COWLES M and DAVIS C. On the origins of the .05 level of statistical significance[J]. American Psychologist, 1982, 37(5): 553–558 doi: 10.1037/0003-066X.37.5.553

王星, 褚挺进. 非参数检验[M]. 第2版, 北京: 清华大学出版社, 2014: 93–99.

WANG Xing and ZHU Tingjin. Non-Parametric Statistics[M]. Second Edition, Beijing: Tsinghua University Press, 2014: 93–99.

MANN H B and WHITNEY D R. On a test of whether one of two random variables is stochastically larger than the other[J]. Annals of Mathematical Statistics, 1947, 18(1): 50–60 doi: 10.1214/aoms/1177730491

LIU Zhongpeng and LIU Lijuan. Bayesian optimization RSSI and indoor location algorithm of iterative least square[J]. International Journal of Smart Home, 2015, 9(6): 31–34 doi: 10.14257/ijsh.2015.9.6.04

施引文献

资源附件(0)

访问统计