Citation: | ZHANG Xuanli, XIU Chundi, WANG Yanzhao, et al. High-precision WiFi indoor localization algorithm based on CSI-XGBoost[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(12): 2536-2544. doi: 10.13700/j.bh.1001-5965.2018.0268(in Chinese) |
Considering the influence of complex indoor environment and multi-path effects on the WiFi fingerprint positioning performance, this paper extracts channel state information (CSI) from the Intel 5300 wireless network card and utilizes the modified CSI amplitude and phase information as fingerprint features. A high-precision fingerprint database was built using the extreme gradient boosting (XGBoost) algorithm to achieve indoor positioning at a decimeter level. Experiments in the actual indoor environment have been conducted to evaluate the effects of sampling interval, line of sight (LOS) and non line of sight (NLOS), missing values, and data dimensions on the localization performance of the proposed method. The results of real indoor experiment show that the proposed CSI-XGBoost method is less affected by NLOS and robust to complex indoor environments. In addition, this method can handle high-dimensional sparse data well and solve the mismatching problem of CSI fingerprinting. Moreover, this method is insensitive to missing data, with localization accuracy of better than 90%.
[1] |
BAHL P, PADMANABHAN V N. RADAR: An in-building RF-based user location and tracking system[C]//Proceedings of IEEE INFOCOM.Piscataway, NJ: IEEE Press, 2000: 775-784.
|
[2] |
YOUSSEF M, AGRAWALA A.The Horus WLAN location determination system[C]//International Conference on Mobile Systems.New York: ACM, 2005: 205-218.
|
[3] |
HALPERIN D, HU W J, SHETH A, et al.802.11 with multiple antenna for dummies[J].ACM SIGCOMM Computer Communication Review, 2010, 40(1):19-25. doi: 10.1145/1672308
|
[4] |
HALPERIN D, HU W J, SHETH A, et al.Predictable 802.11 packet delivery from wireless channel measurements[J] ACM SIGCOMM Computer Communication Review, 2010, 40(10):159-170. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0220057194/
|
[5] |
YANG Z, ZHOU Z M, LIU Y H.From RSSI to CSI:Indoor localization via channel response[J].ACM Computing Surveys, 2013, 46(2):1-32. http://dl.acm.org/citation.cfm?id=2543592
|
[6] |
WANG X Y, GAO L J, MAO S W, et al.DeepFi: Deep learning for indoor fingerprinting using channel state information[C]//2015 IEEE Wireless Communications & Networking Conference.Piscataway, NJ: IEEE Press, 2015: 1666-1671.
|
[7] |
WANG X Y, GAO L J, MAO S W.PhaseFi: Phase fingerprinting for indoor[C]//Proceedings of IEEE Global Communication Conference.Piscataway, NJ: IEEE Press, 2015: 1-6.
|
[8] |
WU K S, XIAO J, YI Y W, et al.CSI-based indoor localization[J].IEEE Transactions on Parallel and Distributed System, 2013, 24(7):1300-1309. doi: 10.1109/TPDS.2012.214
|
[9] |
XIAO J, WU K S, YI Y W, et al.FIFS: Fine-grained indoor fingerprinting system[C]//Proceedings of IEEE ICCCN.Piscataway, NJ: IEEE Press, 2012: 1-7.
|
[10] |
CHAPRE Y, IGNJATOVIC A, SENEVIRATNE A, et al.CSI-MIMO:An effcient WiFi fingerprinting using channel state information with MIMO[J].Pervasive Mobile Computing, 2015, 23:89-103. doi: 10.1016/j.pmcj.2015.07.002
|
[11] |
CHEN T Q, GUESTRIN C.XGBoost: A scale tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York: ACM, 2016: 13-17.
|
[12] |
LAN/MAN Standards Committee.IEEE Standard for Information technology-Telecommunications and information exchange between systems-Local and metropolitan area networks-Specific requirements: Part 11.Wireless LAN medium access control (MAC) and physical layer (PHY) specifications: IEEE 802.11n[S].Piscataway, NJ: IEEE Press, 2009: 312-335.
|
[13] |
SEN S, RADUNOVIC B, CHOUDHURY R R, et al.You are facing the Mona Lisa: Spot localization using PHY layer information[C]//Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services.New York: ACM, 2012: 183-196.
|
[14] |
WANG X Y, GAO L J, MAO S W.CSI phase fingerprinting for indoor localization with a deep learning approach[J].IEEE Internet of Things Journal, 2016, 3(6):1113-1123. doi: 10.1109/JIOT.2016.2558659
|
[15] |
杨萌, 修春娣, 杨东凯.基于感知概率的室内定位系统[J].全球定位系统, 2013, 38(6):238-241. http://www.cnki.com.cn/Article/CJFDTOTAL-CHWZ201404011.htm
YANG M, XIU C D, YANG D K.Indoor positioning system using perceptual probability[J].Global Positioning System, 2013, 38(6):238-241(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-CHWZ201404011.htm
|