|Table of Contents|

[1] Zou Qian, Xia Weiwei, Zhang Jing, Huang Bonan, et al. An indoor positioning system for mobile target trackingbased on VLC and IMU fusion [J]. Journal of Southeast University (English Edition), 2018, 34 (4): 451-458. [doi:10.3969/j.issn.1003-7985.2018.04.006]

An indoor positioning system for mobile target trackingbased on VLC and IMU fusion()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2018 4
Research Field:
Information and Communication Engineering
Publishing date:


An indoor positioning system for mobile target trackingbased on VLC and IMU fusion
Zou Qian Xia Weiwei Zhang Jing Huang Bonan Yan Feng Shen Lianfeng
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
indoor positioning system(IPS) visible light communication(VLC) inertial measurement unit(IMU) hybrid positioning algorithm
An indoor positioning system(IPS)is designed to realize positioning and tracking of mobile targets, by taking advantages of both the visible light communication(VLC)and inertial measurement unit(IMU). The platform of the IPS is designed, which consists of the light-emitting diode(LED)based transmitter, the receiver and the positioning server. To reduce the impact caused by measurement errors, both inertial sensing data and the received signal strength(RSS)from the VLC are calibrated. Then, a practical propagation model is established to obtain the distance between the transmitter and the receiver from the RSS measurements. Furthermore, a hybrid positioning algorithm is proposed by using the adaptive Kalman filter(AKF)and the weighted least squares(WLS)trilateration to estimate the positions of the mobile targets. Experimental results show that the developed IPS using the proposed hybrid positioning algorithm can extend the localization area of VLC, mitigate the IMU drifts and improve the positioning accuracy of mobile targets.


[1] He K, Zhang Y, Zhu Y, et al. A hybrid indoor positioning system based on UWB and inertial navigation [C]// IEEE International Conference on Wireless Communications & Signal Processing. Hefei, China, 2015: 1-5.
[2] van M T, van Tuan N, Son T T, et al. Weighted k-nearest neighbour model for indoor VLC positioning[J]. IET Communications, 2017, 11(6): 864-871. DOI:10.1049/iet-com.2016.0961.
[3] Nakazawa Y, Makino H, Nishimori K, et al. LED-tracking and ID-estimation for indoor positioning using visible light communication [C]// IEEE International Conference on Indoor Positioning and Indoor Navigation. Busan, South Korea, 2015:87-94.
[4] Wu C S, Yang Z, Liu Y H. Smartphones based crowdsourcing for indoor localization[J]. IEEE Transactions on Mobile Computing, 2015, 14(2): 444-457. DOI:10.1109/tmc.2014.2320254.
[5] Li Q L, Wang J Y, Huang T, et al. Three-dimensional indoor visible light positioning system with a single transmitter and a single tilted receiver[J]. Optical Engineering, 2016, 55(10): 106103. DOI:10.1117/1.oe.55.10.106103.
[6] Aghili F, Su C Y. Robust relative navigation by integration of ICP and adaptive Kalman filter using laser scanner and IMU[J]. ASME Transactions on Mechatronics, 2016, 21(4): 2015-2026. DOI:10.1109/tmech.2016.2547905.
[7] Won S H P, Melek W W, Golnaraghi F. A Kalman/particle filter-based position and orientation estimation method using a position sensor/inertial measurement unit hybrid system[J]. IEEE Transactions on Industrial Electronics, 2010, 57(5): 1787-1798. DOI:10.1109/tie.2009.2032431.
[8] Lü H, Feng L H, Yang A Y, et al. High accuracy VLC indoor positioning system with differential detection[J].IEEE Photonics Journal, 2017, 9(3): 1-13. DOI:10.1109/jphot.2017.2698240.
[9] Krommenacker N, Vásquez O C, Alfaro M D, et al. A self-adaptive cell-ID positioning system based on visible light communications in underground mines [C]// 2016 IEEE International Conference on Automatica. Curico, Chile, 2016: 16525691. DOI:10.1109/ICA-ACCA.2016.7778427.
[10] Guan W P, Wu Y X, Wen S S, et al. A novel three-dimensional indoor positioning algorithm design based on visible light communication [J]. Optics Communications, 2017, 392:282-293.
[11] Jimenez Ruiz A R, Seco Granja F, Prieto Honorato J C, et al. Accurate pedestrian indoor navigation by tightly coupling foot-mounted IMU and RFID measurements[J]. IEEE Transactions on Instrumentation and Measurement, 2012, 61(1): 178-189. DOI:10.1109/tim.2011.2159317.
[12] Liu W L. LiDAR-IMU time delay calibration based on iterative closest point and iterated sigma point Kalman filter[J]. Sensors, 2017, 17(3):539. DOI:10.3390/s17030539.
[13] Kumar G A, Patil A K, Patil R, et al. A LiDAR and IMU integrated indoor navigation system for UAVs and its application in real-time pipeline classification[J]. Sensors, 2017, 17(6): 1268. DOI:10.3390/s17061268.
[14] Zhao Y W. Performance evaluation of cubature Kalman filter in a GPS/IMU tightly-coupled navigation system[J]. Signal Processing, 2016, 119: 67-79. DOI:10.1016/j.sigpro.2015.07.014.
[15] Zhang X L, Duan J Y, Fu Y G, et al. Theoretical accuracy analysis of indoor visible light communication positioning system based on received signal strength indicator[J]. Journal of Lightwave Technology, 2014, 32(21): 4180-4186. DOI:10.1109/jlt.2014.2349530.
[16] Komine T, Nakagawa M. Fundamental analysis for visible-light communication system using LED lights[J]. IEEE Transactions on Consumer Electronics, 2004, 50(1): 100-107. DOI:10.1109/tce.2004.1277847.
[17] Nguyen N T, Nguyen N H, Nguyen V H, et al. Improvement of the VLC localization method using the extended Kalman filter [C]// 2014 IEEE Region 10 Conference. Bangkok, Thailand, 2014: 14885864. DOI:10.1109/TENCON.2014.7022416.
[18] Fang X M, Nan L, Jiang Z H, et al.Robust node position estimation algorithms for wireless sensor networks based on improved adaptive Kalman filters[J]. Computer Communications, 2017, 101: 69-81. DOI:10.1016/j.comcom.2016.11.005.
[19] González R, Giribet J I, Patiño H D. An approach to benchmarking of loosely coupled low-cost navigation systems[J]. Mathematical and Computer Modelling of Dynamical Systems, 2014, 21(3): 272-287. DOI:10.1080/13873954.2014.952642.
[20] Zhuang Y, El-Sheimy N. Tightly-coupled integration of WiFi and MEMS sensors on handheld devices for indoor pedestrian navigation[J]. IEEE Sensors Journal, 2016, 16(1): 224-234. DOI:10.1109/jsen.2015.2477444.


Biographies: Zou Qian(1993—), female, graduate; Xia Weiwei(corresponding author), female, doctor, associate professor, wwxia@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61741102, 61471164, 61601122), the Fundamental Research Funds for the Central Universities(No.SJLX_160040).
Citation: Zou Qian, Xia Weiwei, Zhang Jing, et al. An indoor positioning system for mobile target tracking based on VLC and IMU fusion[J].Journal of Southeast University(English Edition), 2018, 34(4):451-458.DOI:10.3969/j.issn.1003-7985.2018.04.006.
Last Update: 2018-12-20