Abstract
In this paper, a novel RSS (received signal strength) based indoor localization scheme is proposed based on multiple antennas cooperation with designed CAA (cylindrical antenna array). The CAA is composed of twelve directional antennas and could receive the signal of twelve dimensions for one tag at the same time. In the offline phase, the RBF (Radial Basis Function) neural network is trained to construct the relationship between received data of twelve dimensions and AOA (angle of arrival). The online positioning phase consists of two steps. In the first step, the AOA is obtained with the trained neural network and subarea is determined to which the unknown tag belongs. In the second step, the triangle localization algorithm is used in the determined subarea to get the accurate tag position. The experiment results show that the proposed approach not only reduces the miss hit rate for the subarea determination to 6.2%, but also provides comparable location accuracy to that of other two conventional RSS-based locating algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Digiampaolo, E., Martinelli, F.: Mobile robot localization using the phase of passive UHF RFID signals. IEEE Trans. Ind. Electron. 61(1), 365–376 (2013)
Cazzorla, A., Angelis, G.D., Moschitta, A., et al.: A 5.6-GHz UWB position measurement system. IEEE Trans. Instrum. Meas. 62(3), 675–683 (2013)
Zhao, X., Xiao, Z., Markham, A., et al.: Does BTLE measure up against WiFi? A comparison of indoor location performance. In: European Wireless 2014, Proceedings of European Wireless Conference, pp. 1–6. VDE (2014)
Wu, C., Yang, Z., Liu, Y., et al.: WILL: wireless indoor localization without site survey. In: 2012 Proceedings of IEEE INFOCOM, pp. 64–72. IEEE (2012)
Wang, J., Katabi, D.: Dude, where’s my card? RFID positioning that works with multipath and non-line of sight. In: ACM SIGCOMM 2013 Conference on SIGCOMM, pp. 51–62. ACM (2013)
Shi, X., Ji, Z.: Indoor positioning system algorithm based on RFID. Univ. Psychol. 12(3), 779–796 (2015)
He, T., Huang, C., Blum, B.M., Stankovic, J.A., Abdelzaher, T.: Range-free localization schemes for large scale sensor networks, pp. 81–95 (2003)
Shi, W., Han, X., Du, K., Li, J.: A novel solution for the optimal deployment of readers in passive RFID location system. In: Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems (2016)
Saab, S.S., Msheik, H.: Novel RFID-based pose estimation using single stationary antenna. IEEE Trans. Ind. Electron. 63(3), 1842–1852 (2016)
Nowak, T., Hartmann, M., Zech, T., Thielecke, J.: A path loss and fading model for RSSI-based localization in forested areas. In: IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, pp. 110–113. IEEE (2016)
Kuo, R.J., Shieh, M.C., Zhang, J.W., et al.: The application of an artificial immune system-based back-propagation neural network with feature selection to an RFID positioning system. Robot. Comput. Integr. Manuf. 29(6), 431–438 (2013)
Miao, K., Chen, Y.D., Xiao, M.: An indoor positioning technology based on GA-BP neural network. In: International Conference on Computer Science & Education, pp. 305–309. IEEE (2011)
Gholami, M., Cai, N., Brennan, R.W.: An artificial neural network approach to the problem of wireless sensors network localization. Robot. Comput. Integr. Manuf. 29(1), 96–109 (2013)
Kuo, R.J., Tseng, W.L., Tien, F.C., et al.: Application of an artificial immune system-based fuzzy neural network to a RFID-based positioning system. Comput. Ind. Eng. 63(4), 943–956 (2012)
Huang, Y.J., Chen, C.Y., Hong, B.W., et al.: Fuzzy neural network based RFID indoor location sensing technique. In: International Joint Conference on Neural Networks, pp. 1–5. IEEE (2010)
Ding, H.L., Ng, W.W.Y., Chan, P.P.K., et al.: RFID indoor positioning using RBFNN with L-GEM. In: International Conference on Machine Learning and Cybernetics, pp. 1147–1152. IEEE (2010)
Moreno-Cano, M.V., Zamora-Izquierdo, M.A., Santa, J., et al.: An indoor localization system based on artificial neural networks and particle filters applied to intelligent buildings. Neurocomputing 122, 116–125 (2013)
Jiménez, A.R., Seco, F., Prieto, J.C., Guevara, J.: Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU. In: Positioning Navigation and Communication, pp. 135–143. IEEE (2010)
Jiang, Y., Xiang, Y., Pan, X., et al.: Hallway based automatic indoor floorplan construction using room fingerprints. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 315–324. ACM (2013)
Wang, B., Zhou, S., Liu, W., Mo, Y.: Indoor localization based on curve fitting and location search using received signal strength. IEEE Trans. Ind. Electron. 62(1), 572–582 (2015)
Guzmánquirós, R., Martínezsala, A., Gómeztornero, J.L., Garcíaharo, J.: Integration of directional antennas in an RSS fingerprinting-based indoor localization system. Sensors 16(1), 4 (2015)
Acknowledgments
This work has been supported by Science and Technology Commission of Shanghai Municipality [Grant No. 17511106902] and ECNU Postgraduate Student Scientific Research Innovation Projects [Grant No. ykc17076].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Jie, W., Minghua, Z., Bo, X. (2018). MAC-ILoc: Multiple Antennas Cooperation Based Indoor Localization Using Cylindrical Antenna Arrays. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-00916-8_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00915-1
Online ISBN: 978-3-030-00916-8
eBook Packages: Computer ScienceComputer Science (R0)