陈锐志, 叶锋. 基于Wi-Fi信道状态信息的室内定位技术现状综述[J]. 武汉大学学报 ( 信息科学版), 2018, 43(12): 2064-2070. doi: 10.13203/j.whugis20180176 引用本文: 陈锐志, 叶锋. 基于Wi-Fi信道状态信息的室内定位技术现状综述[J]. 武汉大学学报 ( 信息科学版), 2018, 43(12): 2064-2070. doi: 10.13203/j.whugis20180176 CHEN Ruizhi, YE Feng. An Overview of Indoor Positioning Technology Based on Wi-Fi Channel State Information[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 2064-2070. doi: 10.13203/j.whugis20180176 Citation: CHEN Ruizhi, YE Feng. An Overview of Indoor Positioning Technology Based on Wi-Fi Channel State Information[J]. Geomatics and Information Science of Wuhan University , 2018, 43(12): 2064-2070. doi: 10.13203/j.whugis20180176 Wi-Fi /  信道状态信息 /  室内定位 / Abstract: Indoor positioning is currently a research hot topic for industrial and scientific communities. Wi-Fi signal has been a common positioning signal adapted researchers. Received signal strength indicator is a traditional measurement used for indoor positioning. It can be easily affected by many factors such as the change of environment. Therefore, it is difficult to achieve such an accuracy that can be used in practice and hard to deploy the positioning solution in a large-scale. More and more resear-chers are now focusing on using channel state information as the measurement, which contains an essential description of Wi-Fi signal propagation and provides more details about the communication channel. This information can be transformed to a useful measurement for positioning. In this paper, we introduce the fundamental description of channel state information and classify the positioning approaches into three categories, which are fingerprinting-based, angle of arrival-based and ranging-based, respectively. The current states of these technologies have been reviewed in details, and the pros and cons have been identified and compared. We conclude the paper with a discussion about the directions in this field. Key words: Wi-Fi /  channel state information /  indoor positioning /  fingerprinting  Bahl P, Padmanabhan V N. RADAR: An In-Buil-ding RF-Based User Location and Tracking System[C]. 19th Joint Conference of the IEEE Computer and Communications Societies, Tel Aviv, Israel, 2000 http://www.researchgate.net/publication/3842777_RADAR_an_in-building_RF-based_user_location_and_tracking_system Hoefel R P F. IEEE 802.11n: On the Performance of Channel Estimation Schemes over OFDM MIMO Spatially-Correlated Frequency Selective Fading TGn Channels[C].XXX Brazilian Symposium on Telecommunications, Brasilia, Brazil, 2012 Halperin D, Hu W, Sheth A, et al. Tool Release:Gathering 802.11n Traces with Channel State Information[J]. ACM Sigcomm Computer Communication Review, 2011, 41(1):53 doi: 10.1145/1925861 Zheng Z W. Channel Estimation and Channel Equa-lization for the OFDM-Based WLAN Systems[C]. International Conference on E-Business and E-Go-vernment, IEEE, Guangzhou, China, 2010 Xie Y, Li Z, Li M. Precise Power Delay Profiling with Commodity WiFi[C]. ACM International Conference on Mobile Computing and Networking, Paris, France, 2015 http://ieeexplore.ieee.org/document/8423070/ Youssef M, Agrawala A. The Horus WLAN Location Determination System[C]. International Conference on Mobile Systems, Applications, and Ser-vices, Washington D C, USA, 2005 doi: 10.1007/s10384-009-0656-9 李桢, 黄劲松.基于RSSI抗差滤波的WiFi定位[J].武汉大学学报·信息科学版, 2016, 41(3):361-366 http://ch.whu.edu.cn/CN/Y2016/V41/I3/361

Li Zhen, Huang Jingsong. WiFi Positioning Using Robust Filtering with RSSI[J]. Geomatics and Information Science of Wuhan University, 2016, 41(3):361-366 http://ch.whu.edu.cn/CN/Y2016/V41/I3/361 Xiao J, Wu K, Yi Y, et al. FIFS: Fine-Grained Indoor Fingerprinting System[C]. International Conference on Computer Communications and Networks, IEEE, Munich, 2012 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6289200 Chapre Y, Ignjatovic A, Seneviratne A, et al. CSI-MIMO: Indoor Wi-Fi Fingerprinting System[C]. 39th Annual IEEE Conference on Local Computer Networks, Edomonton, AB, Canada, 2014 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6925773 Wang X, Gao L, Mao S, et al. DeepFi: Deep Learning for Indoor Fingerprinting Using Channel State Information[C]. Wireless Communications and Networking Conference, IEEE, New Orleans, USA, 2015 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7127718 Choi J S, Lee W H, Lee J H, et al. Deep Learning Based NLOS Identification with Commodity WLAN Devices[J].IEEE Transactions on Vehicular Technology, 2017, doi: 10.1109/TVT.2017.2780121 Huang X, Guo S, Wu Y, et al. A Fine-Grained Indoor Fingerprinting Localization Based on Magnetic Field Strength and Channel State Information[J]. Pervasive & Mobile Computing, 2017, 41, doi: 10.1016/j.pmcj.2017.08.003 Schmidt R. Multiple Emitter Location and Signal Parameter Estimation[J]. IEEE Transactions on Antennas & Propagation, 1986, 34(3):276-280 http://d.old.wanfangdata.com.cn/NSTLHY/NSTL_HYCC027950176/ Roy R, Paulraj A, Kailath T. Estimation of Signal Parameters via Rotational Invariance Techniques-ESPRIT[J]. IEEE Transactions on Acoustics Speech & Signal Processing, 2002, 37(7):984-995 http://d.old.wanfangdata.com.cn/Periodical/shjtdxxb-e201802007 Xiong J, Jamieson K. Array Track: A Fine-Grained Indoor Location System[C]. Usenix Conference on Networked Systems Design and Implementation, USENIX Association, Lombard, IL, 2013 http://dl.acm.org/citation.cfm?id=2482626.2482635 Gjengset J, Xiong J, Mcphillips G, et al. Phaser: Enabling Phased Array Signal Processing on Commodity WiFi Access Points[C]. International Conference on Mobile Computing and Networking. ACM, Hawai, USA, 2014 http://dl.acm.org/authorize?n87317 Kotaru M, Joshi K, Bharadia D, et al. SpotFi:De-cimeter Level Localization Using WiFi[J].ACM Sigcomm Computer Communication Review, 2015, 45(4):269-282 Tzur A, Amrani O, Wool A. Direction Finding of Rogue Wi-Fi Access Points Using an Off-the-Shelf MIMO-OFDM Receiver[J]. Physical Communication, 2015, 17(C):149-164 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=567fd7c3ca74b0e4633105e8eafca2d5 Schüssel M. Angle of Arrival Estimation Using WiFi and Smartphones[C]. International Confe-rence on Indoor Positioning and Indoor Navigation (IPIN), Madrid, Spain, 2016 Wu K, Xiao J, Yi Y, et al. FILA: Fine-Grained Indoor Localization[C]. IEEE INFOCOM, Orlando, USA, 2012 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6195606 Günther A, Hoene C. Measuring Round Trip Times to Determine the Distance Between WLAN Nodes[C]. International Conference on Research in Networking, Reunion Island, France, 2005 http://www.springerlink.com/content/x969830hpygxda7b Ciurana M, Barcelo-Arroyo F, Izquierdo F. A Ranging System with IEEE 802.11 Data Frames[C]. IEEE Radio and Wireless Symposium, Long Beach, CA, 2007 http://ieeexplore.ieee.org/document/4160668/ Giustiniano D, Mangold S. CAESAR: Carrier Sense-Based Ranging in Off-the-Shelf 802.11 Wireless LAN[C]. The 7th Conference on Emerging Networking Experiments and Technologies, Tokyo, Japan, 2011 http://dl.acm.org/citation.cfm?id=2079306 Kumar S, Vasisht D, Katabi D. Decimeter-Level Localization with a Single WiFi Access Point[C]. Usenix Conference on Networked Systems Design and Implementation, USENIX Association, Santa Clara, CA, USA, 2016 http://dl.acm.org/citation.cfm?id=2930623 Xiong J, Sundaresan K, Jamieson K. ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization[C]. International Conference on Mobile Computing and Networking, ACM, Paris, France, 2015 doi: 10.1145/2789168.2790125 Li X, Pahlavan K. Super-Resolution TOA Estimation with Diversity for Indoor Geolocation[J]. IEEE Transactions on Wireless Communications, 2004, 3(1):224-234 doi: 10.1109/TWC.2003.819035

Wi-Fi /

信道状态信息 /

室内定位 /

指纹匹配

摘要: 室内定位技术一直是工业界和学术界的研究热点，Wi-Fi信号作为重要的定位源，长期受到研究人员的关注。传统的利用接收信号强度的Wi-Fi定位方法受到诸多限制，容易受到环境等因素的影响，精度难以有效提升，也无法展开大规模的应用。信道状态信息（channel state information，CSI）是一种比接收信号强度更能描述Wi-Fi信号传播本质的观测量，利用CSI进行室内定位研究已得到越来越多的关注。介绍了CSI基本概念，综述了现有基于CSI的各类定位方法，包括指纹匹配、测角和测距等，分别描述其基本原理，指出其中的优缺点，并分析其现状和难点。并对基于Wi-Fi信道状态信息的定位技术未来的发展方向进行了展望。

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

引用本文: 陈锐志, 叶锋. 基于Wi-Fi信道状态信息的室内定位技术现状综述[J]. 武汉大学学报 ( 信息科学版), 2018, 43(12): 2064-2070. doi: 10.13203/j.whugis20180176 CHEN Ruizhi, YE Feng. An Overview of Indoor Positioning Technology Based on Wi-Fi Channel State Information[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 2064-2070. doi: 10.13203/j.whugis20180176 Citation: CHEN Ruizhi, YE Feng. An Overview of Indoor Positioning Technology Based on Wi-Fi Channel State Information[J]. Geomatics and Information Science of Wuhan University , 2018, 43(12): 2064-2070. doi: 10.13203/j.whugis20180176 随着Wi-Fi技术的发展，IEEE 802.11n系列通信协议及其之后的无线局域网协议应用了多输入多输出(multiple-input multiple-output, MIMO)和正交频分复用(orthogonal frequency division multiplexing，OFDM)等技术，使得Wi-Fi收发设备之间的信道特征可以在物理层进行估计 ^{[ 2 ]} ，并以信道状态信息(channel state information, CSI)的形式存储下来。作为信道频率响应的量化表征，CSI可以反映物理环境中的散射、环境衰减、功率衰减等属性。相比传统的RSSI，CSI是无线信号在空间中传播过程的本质描述，具备更大的应用潜力。

h\left( \tau \right) = \mathop \sum \limits_{i = 1}^N {a_i}{{\rm{e}}^{ - j{\theta _i}}}\delta (\tau - {\tau _i}) H\left( {{f_k}} \right) = \left\| {H\left( {{f_k}} \right)} \right\|{{\rm{e}}^{j\angle H}} {\rm{CS}}{{\rm{I}}_{{\rm{avg}}}} = \mathop \sum \limits_{m = 1}^p \mathop \sum \limits_{n = 1}^q {\rm{cs}}{{\rm{i}}_{mn}} {\mathit{\boldsymbol{R}}_{\mathit{\boldsymbol{XX}}}} = E(\mathit{\boldsymbol{X}}{\mathit{\boldsymbol{X}}^{\rm{H}}}) P\left( \theta \right) = \frac{1}{{{\mathit{\boldsymbol{a}}^{\rm{H}}}\left( \theta \right){\mathit{\boldsymbol{E}}_N}\mathit{\boldsymbol{E}}_N^{\rm{H}}\mathit{\boldsymbol{a}}\left( \theta \right)}} 目前基于CSI的AoA估计主要利用了基站具备多天线的特点，天线之间的相位差跟AoA之间存在明确的关系。由于存在多径等因素，天线数量越多，解算得到的到达角越精确，而出于成本和体积等各方面因素考虑，现有的商用无线网卡或Wi-Fi设备通常不配置较多天线。基于现存的Wi-Fi设备实现应用仍是较有效的推广手段，随着Wi-Fi协议的发展，以及天线设计与工艺的进步，未来可利用的天线数量将更多，AoA方法的精度也将进一步提高。同时，AoA方法基于严格的数学模型，所利用相位信息的精度将直接影响结果的准确性，虽然包括Phaser和SpotFi等在内的诸多系统对从CSI中提取的相位进行了一定程度的误差处理，但由于获取的原始数据受到平台及工具等各方面限制，误差源及其处理方法还有进一步讨论的空间。

\begin{array}{l} \forall i \in \left\{ {1, 2 \cdots n} \right\}, \tau = - \angle {h_i}/2{\rm{ \mathsf{ π} }}{f_i}\;\;\;\bmod 1/{f_i}\\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; \ldots \end{array}