摘要： 室内定位是未来人工智能的核心技术之一，对即将到来的人工智能时代起着举足轻重的作用。开发有效的室内定位新技术是工业界和学术界的研究热点，如谷歌研发的室内视觉定位服务技术、苹果致力推动的基于低功耗蓝牙的iBeacon室内定位技术以及百度携手芬兰IndoorAtlas公司推出的基于磁场匹配的室内定位方案等。然而，受室内复杂环境以及空间布局、拓扑易变等影响，实现准确、可靠、实时的室内定位，满足各类定位需求仍有很大的挑战性。目前，随着智能手机的普及和微机电系统技术的发展，智能手机内置多种传感器和支持丰富的射频信号，可提供不同的定位源。本文从智能手机的内置传感器和射频信号两个方面，综述了现有基于智能手机的室内定位技术，指出各种定位技术的优缺点和应用场景，分析室内定位的发展现状和存在难点，对室内定位技术未来的发展方向进行了展望。

Abstract: Indoor positioning is one of the core technologies of artificial intelligence (AI) in the future and will play a pivotal role in the upcoming era of AI. Currently, indoor positioning is one of the hot research topics in academic and industrial society. Google, as one of the leading information technology (IT) companies, has listed visual positioning service (VPS) as one of the core technologies. Apple has endeavored to prompt iBeacon, the low energy Bluetooth technology for indoor positioning. In cooperation with a Finnish company, IndoorAltas, Baidu launched an indoor positioning program with a magnetic matching approach. All these initiatives and new technologies have shown the significance and necessaries of indoor positioning. However, affected by the complexity of the indoor spaces, it is still challenging to achieve accurate, effective, full coverage and real-time positioning solution indoors. With the popularity of smart phones and the rapid development of MEMS sensors in recent years, many methods have been proposed to use the smartphone built-in sensors and RF radios for indoor positioning. In this paper, we focus on indoor positioning technologies for smartphones and classify the different technologies into two categories, namely the radio frequency (RF) technologies and the sensors technologies. The state-of-the-art of the technologies has been reviewed. The pros and cons of the technologies have been commented in the context of different application scenarios. Moreover, the challenges of indoor positioning have also been pointed out and the directions of the future development of this area have been discussed.

Key words: smartphone, indoor positioning, location-based services, radio frequency (RF) signals, microelectromechanical systems (MEMS) [1] WEISER M. The Computer for the 21st Century[J]. Scientific American, 1991, 265(3):94-104.
[2] KAPLAN E D, HEGARTY C J. Understanding GPS, Principles and Applications[M]. 2nd ed. Boston:Artech House, 2006.
[3] 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.
[4] MAUTZ R. Indoor Positioning Technologies[D]. Swissland:ETH Zurich, 2012.
[5] 刘克强. 基于室内位置与多维情境的人类活动识别方法研究[D]. 徐州:中国矿业大学, 2017. LIU Keqiang. Study on Human Activity Recognition Method Based on Indoor Location and Multiple Context[D]. Xuzhou:China University of Mining & Technology, 2017.
[6] IEEE Standard Association. IEEE 802.11 Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications[S].[S.l.]:IEEE, 2012.
[7] 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.
[8] SCHAUER L, DORFMEISTER F, MAIER M. Potentials and Limitations of WIFI-Positioning Using Time-of-Flight[C]//Proceedings of the 2013 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Montbeliard-Belfort, France:IEEE, 2013:1-9.
[9] BAHL P, PADMANABHAN V N. Radar:An in-building RF-based User Location and Tracking System[C]//Proceedings of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2000. Tel Aviv, Israel:IEEE, 2000:775-784.
[10] https://www.ekahau.com.
[11] YOUSSEF M, AGRAWALA A. The Horus WLAN Location Determination System[C]//Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services. Seattle, Washington:ACM, 2005:205-218.
[12] WANG Xuyu, GAO Lingjun, MAO Shiwen, et al. CSI-based Fingerprinting for Indoor Localization:A Deep Learning Approach[J]. IEEE Transactions on Vehicular Technology, 2017, 66(1):763-776.
[13] WANG Xuyu, GAO Lingjun, MAO Shiwen, et al. DeepFi:Deep Learning for Indoor Fingerprinting Using Channel State Information[C]//Proceedings of the 2015 IEEE Wireless Communications and Networking Conference. New Orleans, LA:IEEE, 2015:1666-1671.
[14] 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.
[15] ZHAO Hailong, HUANG Baoqi, JIA Bing. Applying Kriging Interpolation for WiFi Fingerprinting Based Indoor Positioning Systems[C]//Proceedings of the 2016 IEEE Wireless Communications and Networking Conference. Doha, Qatar:IEEE, 2016:1-6.
[16] ZHUANG Yuan, SYED Z, LI You, et al. Evaluation of Two WiFi Positioning Systems Based on Autonomous Crowdsourcing of Handheld Devices for Indoor Navigation[J]. IEEE Transactions on Mobile Computing, 2016, 15(8):1982-1995.
[17] TANG Jian, CHEN Yuwei, CHEN Liang, et al. Fast Fingerprint Database Maintenance for Indoor Positioning Based on UGV SLAM[J]. Sensors, 2015, 15(3):5311-5330.
[18] CHEN Liang, THOMBRE S, JÄRVINEN K, et al. Robustness, Security and Privacy in Location-based Services for Future IoT:A Survey[J]. IEEE Access, 2017, 5:8956-8977.
[19] VASISHT D, KUMAR S, KATABI D. Decimeter-level Localization with a Single WiFi Access Point[C]//Proceedings of the 13th Usenix Conference on Networked Systems Design and Implementation. Santa Clara, CA:ACM, 2016:165-178.
[20] KOTARU M, JOSHI K, BHARADIA D, et al. Spotfi:Decimeter Level Localization Using WiFi[C]//Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication. London, United Kingdom:ACM, 2015:269-282.
[21] IEEE Standard Association. IEEE 802.15 Wireless Personal Area Networks (PANs)[S].[S.l.]:IEEE, 2005.
[22] Apple Company. Getting Started with iBeacon Version 1.0[M]. 2014.
[23] CHEN Liang, PEI Ling, KUUSNIEMI H, et al. Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints[J]. Wireless Personal Communications, 2013, 70(4):1735-1745.
[24] CHEN Liang, KUUSNIEMI H, CHEN Yuwei, et al. Constraint Kalman Filter for Indoor Bluetooth Localization[C]//Proceedings of the 23rd European Signal Processing Conference (EUSIPCO). Nice, France:IEEE, 2015:1915-1919.
[25] CHEN Liang, KUUSNIEMI H, CHEN Yuwei, et al. Information Filter with Speed Detection for Indoor Bluetooth Positioning[C]//Proceedings of the 2011 International Conference on Localization and GNSS (ICL-GNSS). Tampere, Finland:IEEE, 2011:47-52.
[26] http://quuppa.com/company/.
[27] OZDENIZCI B, COSKUN V, OK K. NFC Internal:An Indoor Navigation System[J]. Sensors, 2015, 15(4):7571-7595.
[28] ZHAO Yilin. Standardization of Mobile Phone Positioning for 3G Systems[J]. IEEE Communications Magazine, 2002, 40(7):108-116.
[29] LAKMALI B D S, DIAS D. Database Correlation for GSM Location in Outdoor & Indoor Environments[C]//Proceedings of the 4th International Conference on Information and Automation for Sustainability (ICIAFS). Colombo, Sri Lanka:IEEE, 2008:42-47.
[30] Ericsson Research Blog. Indoor Positioning in LTE[EB/OL]. (2015-07-09). https://www.ericsson.com/research-blog/lte/indoor-positioning-in-lte/.
[31] NGMN Alliance. 5G White Paper[R].[S.l.]:NGMN, 2015. https://www.ngmn.org/uploads/media/NGMN_5G_White_Paper_V1_0.pdf.
[32] 3GPP TR 38.913. Study on Scenarios and Requirements for Next Generation Access Technologies[R]. https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=2996.
[33] KOIVISTO M, COSTA M, WERNER J, et al. Joint Device Positioning and Clock Synchronization in 5G Ultra-Dense Networks[J]. IEEE Transactions on Wireless Communications, 2017, 16(5):2866-2881.
[34] KELA P, COSTA M, TURKKA J, et al. Location Based Beamforming in 5G Ultra-dense Networks[C]//Proceedings of the 84th Vehicular Technology Conference (VTC-Fall. Montreal, QC, Canada:IEEE, 2016:1-7.
[35] WANT R, HOPPER A, FALCO V, et al. The Active Badge Location System[J]. ACM Transactions on Information Systems (TOIS), 1992, 10(1):91-102.
[36] http://www.eet-china.com/news/article/201703070600.
[37] GANICK A, RYAN D. Light Positioning System Using Digital Pulse Recognition:US, 8248467[P]. 2012-08-21.
[38] LIU Jingbin, CHEN Yuwei, JAAKKOLA A, et al. The Uses of Ambient Light for Ubiquitous Positioning[C]//Proceedings of the 2014 IEEE/ION Position, Location and Navigation Symposium-PLANS 2014. Monterey, CA:IEEE, 2014:102-108.
[39] WARD A, JONES A, HOPPER A. A New Location Technique for the Active Office[J]. IEEE Personal Communications, 1997, 4(5):42-47.
[40] PRIYANTHA N B, CHAKRABORTY A, BALAKRISHNAN H. The Cricket Location-Support System[C]//Proceedings of the 6th Annual International Conference on Mobile Computing and Networking. Boston, MA:ACM, 2000:32-43.
[41] http://toutiao.secjia.com/tracking-user-behavior-with-ultrasound.
[42] CHEN R, PEI L, CHEN Y. A Smart Phone Based PDR Solution for Indoor Navigation[C]//Proceedings of the 24th International Technical Meeting of the Satellite Division of the Institute of Navigation. Portland, OR:ION, 2011.
[43] THOMPSON S. IndoorAtlas[Z]. http://www.indooratlas.com.
[44] RUOTSALAINEN L, KUUSNIEMI H, BHUIYAN M Z H, et al. A Two-dimensional Pedestrian Navigation Solution Aided with a Visual Gyroscope and a Visual Odometer[J]. GPS Solutions, 2013, 17(4):575-586.
[45] RUOTSALAINEN L. Visual Gyroscope and Odometer for Pedestrian Indoor Navigation with A Smartphone[C]//Proceedings of the 25th International Technical Meeting of the Satellite Division of the Institute of Navigation. Nashville, TN:Nashville Convention Center, 2012.
[46] RUOTSALAINEN L, KIRKKO-JAAKKOLA M, CHEN Liang, et al. Multi-Sensor SLAM for Tactical Situational Awareness[C]//Proceedings of the 2016 International Technical Meeting of The Institute of Navigation. Monterey, CA:Hyatt Regency Monterey, 2016:273-282.
[47] KIM B, KWAK M, LEE J, et al. A Multi-pronged Approach for Indoor Positioning with WiFi, Magnetic and Cellular Signals[C]//Proceedings of the 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Busan, South Korea:IEEE, 2014:723-726.
[48] KARLSSON F, KARLSSON M, BERNHARDSSON B, et al. Sensor Fused Indoor Positioning Using Dual Band WiFi Signal Measurements[C]//Proceedings of the 2015 European Control Conference. Linz, Austria:IEEE, 2015:1669-1672.
[49] JEON J S, KONG Y, NAM Y, et al. An Indoor Positioning System Using Bluetooth RSSI with an Accelerometer and a Barometer on a Smartphone[C]//Proceedings of the 10th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA). Krakow, Poland:IEEE, 2015:528-531.
[50] LI Xin, WANG Jian, LIU Chunyan, et al. Integrated WiFi/PDR/Smartphone Using an Adaptive System Noise Extended Kalman Filter Algorithm for Indoor Localization[J]. ISPRS International Journal of Geo-Information, 2016, 5(2):8.
[51] LI You, ZHUANG Yuan, LAN Haiyou, et al. A Hybrid WiFi/Magnetic Matching/PDR Approach for Indoor Navigation with Smartphone Sensors[J]. IEEE Communications Letters, 2016, 20(1):169-172.
[52] WANG Jian, HU Andong, LIU Chunyan, et al. A Floor-map-aided WiFi/Pseudo-odometry Integration Algorithm for an Indoor Positioning System[J]. Sensors, 2015, 15(4):7096-7124.
[53] PEI Ling, LIU Jingbin, GUINNESS R, et al. The Evaluation of WIFI Positioning in a Bluetooth and WiFi Coexistence Environment[C]//Proceedings of the 2012 Ubiquitous Positioning, Indoor Navigation, and Location Based Service (UPINLBS). Helsinki, Finland:IEEE, 2012:1-6.
[54] SCHMITT S, ADLER S, KYAS M. The Effects of Human Body Shadowing in RF-based Indoor Localization[C]//Proceedings of the 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Busan, South Korea:IEEE, 2014:307-313.
[55] ZHANG Rui, BANNOURA A, HÖFLINGER F, et al. Indoor Localization Using a Smart Phone[C]//Proceedings of the 2013 IEEE Sensors Applications Symposium (SAS). Galveston, TX:IEEE, 2013:38-42.
[56] QUIGLEY M, STAVENS D, COATES A, et al. Sub-Meter Indoor Localization in Unmodified Environments with Inexpensive Sensors[C]//Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. Taipei:IEEE, 2010:2039-2046.
[57] XIE Bo, CHEN Kongyang, TAN Guang, et al. LIPS:A Light Intensity-based Positioning System for Indoor Environments[J]. ACM Transactions on Sensor Networks (TOSN), 2016, 12(4):28.
[58] XIE Bo, GONG Shimin, TAN Guang. LiPro:Light-Based Indoor Positioning with Rotating Handheld Devices[J]. Wireless Networks, 2016. DOI:10.1007/s11276-016-1312-1. (in Press)
[59] CHEN Ruizhi, CHU Tianxing, LIU Keqiang, et al. Inferring Human Activity in Mobile Devices by Computing Multiple Contexts[J]. Sensors, 2015, 15(9):21219-21238.
[60] CHEN Ruizhi, CHEN Wei, CHEN Xiang, et al. Sensing Strides Using EMG Signal for Pedestrian Navigation[J]. GPS Solutions, 2011, 15(2):161-170. 陈锐志, 钱隆, 牛晓光, 徐诗豪, 陈亮, 裘超. 基于数据与模型双驱动的音频/惯性传感器耦合定位方法 [J]. 测绘学报, 2022, 51(7): 1160-1171. 李广云, 孙森震, 王力, 冯其强. 可见光通信室内定位技术进展与应用 [J]. 测绘学报, 2022, 51(6): 909-922. 李俊生, 高敏, 张兵, 张方方, 王胜蕾, 殷子瑶, 谢娅. 智能手机影像支持的水质监测算法与应用 [J]. 测绘学报, 2022, 51(4): 568-576. 杨高朝, 王庆, 蔚保国, 刘鹏飞, 李爽. 基于抗差LM的视觉惯性里程计与伪卫星混合高精度室内定位 [J]. 测绘学报, 2022, 51(1): 18-30. 薛卫星, 李清泉, 周宝定. 虚拟AP支持的室内非线性约束区域定位 [J]. 测绘学报, 2021, 50(5): 569-579. 陈锐志, 郭光毅, 叶锋, 钱隆, 徐诗豪, 李正. 智能手机音频信号与MEMS传感器的紧耦合室内定位方法 [J]. 测绘学报, 2021, 50(2): 143-152. 张星, 林静, 李清泉, 刘涛, 方志祥. 结合感知哈希与空间约束的室内连续视觉定位方法 [J]. 测绘学报, 2021, 50(12): 1639-1649. 李清泉, 周宝定, 马威, 薛卫星. GIS辅助的室内定位技术研究进展 [J]. 测绘学报, 2019, 48(12): 1498-1506. 单杰. 从专业遥感到大众遥感 [J]. 测绘学报, 2017, 46(10): 1434-1446. 陈国良, 张言哲, 汪云甲, 孟晓林. WiFi-PDR室内组合定位的无迹卡尔曼滤波算法 [J]. 测绘学报, 2015, 44(12): 1314-1321. 吴东金, 夏林元. 面向室内WLAN定位的动态自适应模型 [J]. 测绘学报, 2015, 44(12): 1322-1330. 地址：北京市西城区三里河路50号　邮编：100045
电话：010-68531192,68531322,68531162,68531338　 传真：010-68531317　 Email：chxb@ chinajournal.net.cn
网 址： http://xb.sinomaps.com
本系统由 北京玛格泰克科技发展有限公司 设计开发　技术支持：[email protected]