APP下载

基于RSS与CSI混合指纹室内定位研究

2018-01-15于海涛李治军姜守旭

智能计算机与应用 2017年6期
关键词:信号强度无线网络指纹

于海涛+李治军+姜守旭

摘要: 关键词: 中图分类号: 文献标志码: A文章编号: 2095-2163(2017)06-0148-04

Abstract: The receiving signal strength indicator (RSS) as a mainstream solution is often used for locating system and fingerprint positioning system based on ranging. However, RSS is often affected by multiple size effects and noise signals, and its location performance is not stable. In recent years, many commercial WiFi devices have supported access to the physical layer's channel status information (CSI). CSI is a more finegrained indicator of signal characteristics than RSS. Compared to RSS, CSI analyses the characteristics of multiple subcarrier signals to avoid the effects of multipath effect and noise. The CSI has opened up new spaces for WiFi based indoor location technology, and has been concerned by researchers. For this purpose, this paper carries out the research on the indoor location method based on RSS and CSI hybrid fingerprint.

0引言

随着WiFi网络的密集部署以及智能移动设备的普及,基于WiFi通讯的无线网络变得越来越重要。在无线网络环境下,人类活动会影响通讯信号及信号特征,所以通讯信号除了用于滿足正常的通信需求外,还可以通过分析信号来挖掘出人类活动信息的内容,从而更好地利用无线网络,室内定位就是其典型应用之一。目前,利用WiFi信号进行室内定位的方法主要可以分为三类:指纹法(fingerprinting-based)、测距法(ranging-based)、到达角度法(angle of arrival (AOA)-based)。其中,测距法通过计算待定位目标与至少三个不同AP之间的距离并利用几何模型进行定位,而测距法又可以分为两类:基于信号强度、基于时间(TOF)。进一步研究可知,基于信号强度方法利用多个接受信号训练信号强度衰落模型中的参数,从而得到距离;基于时间方法与之类似,也是通过计算信号传播时间求出距离。但是,上述两种方法需要AP与定位目标之间存在LOS通讯路径。本文的室内定位研究选用了基于RSS与CSI的混合指纹,使用混合指纹进行定位相比其他基于单一指纹信息(RSS或CSI)的定位方法有很多好处。由于多径效应的影响,RSS信息不稳定,即使在固定位置采集得到的RSS信息也会随时间不断剧烈变化,并且RSS并没有包含OFDM下多子载波的相应多径信息。OFDM系统中,相比RSS信息,CSI利用了不同子载波的信号传输过程信息,从而可以降低多径效应的影响。通过细粒度的CSI指纹法,可以在不增加数据采集成本的前提下,改善室内定位精度。因此本次研究利用CSI和RSS混合指纹来进行室内定位的设计实现。

1RSS初步定位

1.1spike剔除

如图1所示,不同颜色的折线代表不同AP对应beacon包的RSS值,横轴为时间,纵轴为信号强度。从图1中可以看出,原始RSS数据基本保持稳定,但是存在某些不规律的信号突变,而这些信号突变往往导致RSS大幅度降低,研究将这类大幅变化称为spike。这些spike并不能真实反映信号强度在空间上的分布。无论在离线指纹数据库建立阶段,还是在线采集样本指纹时,都需要去除spike的影响。所以就需要识别spike并剔除其影响。为此提出了一个简单的基于滑动时间窗统计的spike检测与恢复方法。时间窗长度为1 s,统计时间窗内最小RSS与其他RSS均值的差值。若差值的绝对值大于一定的阈值,就可判定该最小RSS对应的beacon受到spike影响,则去除该beacon的RSS值,并恢复为当前时间窗内其它beacon的RSS均值。实验效果如图2所示,恢复后的RSS数据在保留了原有大部分数据的同时,去除了spike的影响。

1.2缺失beacon对应RSS恢复

由于802.11n中载波侦听机制(CSMA/CA)的存在,在信道高负载无线网络环境下,由于在一大段时间内的信道繁忙而导致AP的beacon缺失。实际生活中,大量WiFi设备无法及时侦测到AP也是由以上原因所导致。如图3所示,不同颜色的折线代表不同AP对应的beacon的RSS信号随时间的变化,图3表明:三个AP对应的beacon在622 s之后的近2 s内缺失,2 s的beacon缺失将会对实时要求较高的室内定位产生较大的影响。为了避免beacon缺失引发的后果,从而尽量减少未侦测AP信息带来的损失,需要对其相应AP的RSS信息进行恢复。

图4是对某一网格内的AP信号进行主成分分析的结果,可以看出该网格内的不同AP信号强度具有鲜明的线性相关性、数据低秩性。所以,研究可以利用基于矩阵分解的低秩数据回复算法对丢失beacon的AP的RSS信号提供恢复处理。为此,则选取了基于奇异值分解的算法。为了尽量减小计算时间,过程中首先利用未丢失的AP的RSS组成的向量与指纹数据库中相应AP的RSS向量进行比较,选取余弦距离较小的top-k个指纹参与矩阵分解。最终可得本文设计给出的方法恢复得到的RSS相对误差为20.7%。endprint

1.3離线阶段

1.4在线阶段

2CSI精确定位

2.1深度神经网络结构

利用CSI进行精确定位的时候用到了深度神经网络系统,这里选用的是tensorflow系统神经网络,考虑到神经网络的强大的学习能力,原有的3*3*30的270维度特征的建模在精确度上仍有所欠缺,因此重点择取深度学习进行特征学习,其中的数据输入是270维的CSI数据特征,通过把标签换成对应的CSI输入数据,这样就开始了深度学习训练。可以使用表征数据内部特征的深度网络DFDN。对于每一个APi及单位区域 j, 均可以得到表征数据的内部特征的深度神经网DFDN(i, j)。图5即完整展示了深度神经网络的训练过程。由图5可知,该网络共有6层,其中每一层的相关设置都在ubuntu的tensorflow深度学习框架下面获得定制实现。

4结束语

本文提出了一种基于RSS与CSI混合指纹室内定位研究方法。展开来说,本次研究首先给出了基于RSS初步定位的设计解析和功能实现;同时,又重点探讨了基于CSI精确定位的分析模式与方法流程。在此基础上,进一步论述展示了基于RSS与CSI混合指纹室内定位的研发仿真结果。关于本课题的深入研究还在不断的发展进程中,本文的研究成果也可为后续的同类研究提供有益的借鉴与参考。

参考文献:

[1] LIU Hui, DARABI H, BANERJEE P, et al. Survey of wireless indoor positioning techniques and systems[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2007, 37( 6):1067-1080.

[2] DAYEKH S, AFFES S, KANDIL N, et al. Cooperative localization in mines using fingerprinting and neural networks[C]//2010 IEEE Wireless Communication and Networking Conference. Sydney, NSW, Australia:IEEE, 2010:1-6.

[3] WU Zhili, LI C H, NG J K ,et al. Location estimation via support vector regression[J]. IEEE Transactions on Mobile Computing, 2007, 6(3):311-321.

[4] WU Kaishun, XIAO Jiang, YI Youwen, et al. CSI-based indoor localization[J]. IEEE Transactions on Parallel and Distributed Systems, 2013,24(7):1300-1309.

[5] HALPERIN D, HU Wenjun, SHETH A, et al. Predictable 802.11 packet delivery from wireless channel measurements[C]//Proceedings of the ACM SIGCOMM 2010 conference. New Delhi, India:ACM, 2010:159-170.

[6] WANG Xuyu, GAO Lingjun, MAO Shiwen, et al. DeepFi: Deep learning for indoor fingerprinting using channel state information[C]//2015 IEEE Wireless Communications and Networking Conference (WCNC). New Orleans, LA, USA:IEEE, 2015:1666-1671.

[7] WANG Xuyu, GAO Li^ngjun, MAO Shiwen. PhaseFi: Phase fingerprinting for indoor localization with a deep learning approach[C]//2015 IEEE Global Communications Conference (GLOBECOM) . San Diego, CA, USA:IEEE, 2015:1-6.

[8] WANG Xuyu, GAO Lingjun, MAO Shiwen. CSI phase fingerprinting for indoor localization with a deep learning approach[J]. IEEE Internet of Things Journal,2016,3(6):1113-1123.

[9] XIONG J, JAMIESON K. Arraytrack: A finegrained indoor location system[J]. USENIX Symposium on Networked Systems Design and Implementation, 2013(279976): 71-84.

[10]HAN D, JUNG S, LEE M, et al. Building a practical WiFibased indoor navigation system[J]. IEEE Pervasive Computing, 2014,13(2): 72-79.endprint

[11]MIROWSKI P, WHITING P, STECK H. Probability kernel regression for WiFi localisation[J]. Journal of Location Based Services, 2012,6(2): 81-100.

[12]CHO H, SANG W K. Mobile robot localization using biased chirpspreadspectrum ranging[J]. IEEE transactions on industrial electronics,2010,57(8): 2826-2835.

[13]DHITAL A, CLOSAS P, FERNNDEZPRADES C. Bayesian filtering for indoor localization and tracking in wireless sensor networks[J]. Eurasip Journal on Wireless Communications and Networking, 2012,2012(1): 1-13.

[14]ZHANG Xinyu, SHIN K G. Cooperative carrier signaling: Harmonizing coexisting WPAN and WLAN devices[J]. IEEE/ACM Transactions on Networking, 2013, 21(2):426-439.

[15]CHEBROLU K, DHEKNE A. Esense: Communication through energy sensing[C]// Proceedings of the 15th annual international conference on Mobile computing and networking. Beijing, China: ACM, 2009:85-96.

[16]KIM S M, TIAN He. FreeBee: Crosstechnology communication via free sidechannel[C]//Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. Paris, France:ACM, 2015:317-330.

[17]BHARADIA D, JOSHI K R, KOTARU M, et al. Backfi: High throughput WiFi backscatter[C]//Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication. London, United Kingdom:ACM,2015: 283-296.

[18]IYER V, TALLA V, KELLOGG B, et al. Intertechnology backscatter: Towards Internet connectivity for implanted devices[C]// Proceedings of the 2016 ACM SIGCOMM Conference. Florianopolis, Brazil:ACM, 2016:356-369.

[19]ISHIDA S, IZUMI K, TAGASHIRA S, et al. WiFi APRSS monitoring using sensor nodes toward anchorfree sensor localization[C]//2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall). Boston, MA, USA:IEEE, 2015:1-5.

[20]NIU Jianwei, WANG Bowei, SHU Lei, et al. ZIL: An energyefficient indoor localization system using ZigBee radio to detect WiFi fingerprints[J]. IEEE Journal on Selected Areas in Communications,2015, 33 (7): 1431-1442.

[21]WANG Yufei, WANG Qixin, ZENG Zheng, et al. WiCop: Engineering WiFi temporal whitespaces for safe operations of wireless body area networks in medical applications[C]// 2011 IEEE 32nd Real-Time Systems Symposium. Vienna, Austria:IEEE, 2011:170-179.

[22]BAHL P, PADMANABHAN V N. Radar: An inbuilding RFbased user location and tracking system[C]// INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Tel Aviv, Israel:IEEE, 2000,2:775-784.endprint

[23]CHAI Xiaoyong, YANG Qiang. Reducing the calibration effort for probabilistic indoor location estimation[J]. IEEE Transactions on Mobile Computing, 2007,6(6): 649-662.

[24]LI B, WANG Y, LEE H K, et al. Method for yielding a database of location fingerprints in WLAN[J]. IEE ProceedingsCommunications,2005, 152(5): 580-586.

[25]KUO S P, TSENG Y C. Discriminant minimization search for largescale RFbased localization systems[J]. IEEE Transactions on mobile computing,2011, 10(2): 291-304.

[26]PERING T, AGARWAL Y, GUPTA R, et al. Coolspots: Reducing the power consumption of wireless mobile devices with multiple radio interfaces[C]//Proceedings of the 4th international conference on Mobile systems, applications and services. Uppsala, Sweden:ACM, 2006:220-232.

[27]LI Qiang, ZHANG Ying, LIN Jingran, et al. Fullduplex bidirectional secure communications under perfect and distributionally ambiguous eavesdropper's CSI[J]. arXiv preprint arXiv:1705.07337v2, 2017.

[28]LI R T H, CHUNG H S H, CHAN T K M. An active modulation technique for singlephase grid connected CSI[J]. IEEE Transactions on Power Electronics,2007,22(4):1373-1382.endprint

猜你喜欢

信号强度无线网络指纹
为什么每个人的指纹都不一样
无线网络安全漏洞及防范策略
web3.0时代,无线网络安全策略研究与防范
唯一的指纹
4G无线网络建设项目的进度管理浅析
钻铤对随钻电磁波测井信号的影响分析
TETRA数字集群通信系统在露天矿山的应用
可疑的指纹
以WiFi和ZigBee联合定位的消防灭火救援系统
是时候转换到全无线网络了吗