基于信號分布混合假設(shè)檢驗(yàn)的Wi-Fi室內(nèi)定位方法
doi: 10.11999/JEIT180147 cstr: 32379.14.JEIT180147
-
重慶郵電大學(xué)移動通信技術(shù)重慶市重點(diǎn)實(shí)驗(yàn)室 ??重慶 ??400065
基金項(xiàng)目: 國家自然科學(xué)基金(61771083, 61704015),長江學(xué)者和創(chuàng)新團(tuán)隊(duì)發(fā)展計劃基金(IRT1299),重慶市科委重點(diǎn)實(shí)驗(yàn)室專項(xiàng)經(jīng)費(fèi)基金,重慶市基礎(chǔ)與前沿研究計劃基金(cstc2017jcyjAX0380, cstc2015jcyjBX0065),重慶市高校優(yōu)秀成果轉(zhuǎn)化基金(KJZH17117),重慶市研究生科研創(chuàng)新項(xiàng)目(CYS17221),重慶市教委科學(xué)技術(shù)研究項(xiàng)目(KJ1704083)
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: Wi-Fi室內(nèi)定位技術(shù)是目前移動計算領(lǐng)域的研究熱點(diǎn)之一,而傳統(tǒng)位置指紋定位方法沒有考慮復(fù)雜室內(nèi)環(huán)境下Wi-Fi信號分布的多樣性問題,從而導(dǎo)致Wi-Fi室內(nèi)定位系統(tǒng)的魯棒性較差。為了解決這一問題,該文提出一種基于信號分布混合假設(shè)檢驗(yàn)的Wi-Fi室內(nèi)定位方法。首先根據(jù)Jarque-Bera(JB)檢驗(yàn)結(jié)果對各個參考點(diǎn)處的Wi-Fi信號分布進(jìn)行正態(tài)性評價;然后針對不同Wi-Fi信號分布特性,利用混合Mann-Whitney U檢驗(yàn)/T檢驗(yàn)方法構(gòu)造匹配參考點(diǎn)集合,以實(shí)現(xiàn)對目標(biāo)的區(qū)域定位;最后通過計算定位區(qū)域中匹配參考點(diǎn)的K近鄰(K-Nearest Neighbor, KNN),完成對目標(biāo)的位置坐標(biāo)估計。實(shí)驗(yàn)結(jié)果表明,所提方法相比于傳統(tǒng)Wi-Fi室內(nèi)定位方法具有更高的定位精度和更強(qiáng)的系統(tǒng)魯棒性。
-
關(guān)鍵詞:
- 室內(nèi)定位 /
- Jarque-Bera檢驗(yàn) /
- Mann-Whitney U檢驗(yàn) /
- T檢驗(yàn) /
- 信號統(tǒng)計分布
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.-
Key words:
- Indoor localization /
- Jarque-Bera test /
- Mann-Whitney U test /
- T-test /
- Signal statistical distribution
-
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 劉洺辛, 孫建利. 基于能效的WLAN室內(nèi)定位系統(tǒng)模型設(shè)計與實(shí)現(xiàn)[J]. 儀器儀表學(xué)報, 2014, 35(5): 1169–1178 doi: 10.19650/j.cnki.cjsi.2014.05.029LIU 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 吳楠, 王旭東, 胡晴晴. 基于多LED的高精度室內(nèi)可見光定位方法[J]. 電子與信息學(xué)報, 2015, 37(3): 727–732 doi: 10.11999/JEIT140725WU 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, 褚挺進(jìn). 非參數(shù)檢驗(yàn)[M]. 第2版, 北京: 清華大學(xué)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 -