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http://dx.doi.org/10.3837/tiis.2021.01.019

Indoor 3D Dynamic Reconstruction Fingerprint Matching Algorithm in 5G Ultra-Dense Network  

Zhang, Yuexia (Beijing Information Science & Technology University)
Jin, Jiacheng (Beijing Information Science & Technology University)
Liu, Chong (Beijing Information Science & Technology University)
Jia, Pengfei (Beijing Information Science & Technology University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.1, 2021 , pp. 343-364 More about this Journal
Abstract
In the 5G era, the communication networks tend to be ultra-densified, which will improve the accuracy of indoor positioning and further improve the quality of positioning service. In this study, we propose an indoor three-dimensional (3D) dynamic reconstruction fingerprint matching algorithm (DSR-FP) in a 5G ultra-dense network. The first step of the algorithm is to construct a local fingerprint matrix having low-rank characteristics using partial fingerprint data, and then reconstruct the local matrix as a complete fingerprint library using the FPCA reconstruction algorithm. In the second step of the algorithm, a dynamic base station matching strategy is used to screen out the best quality service base stations and multiple sub-optimal service base stations. Then, the fingerprints of the other base station numbers are eliminated from the fingerprint database to simplify the fingerprint database. Finally, the 3D estimated coordinates of the point to be located are obtained through the K-nearest neighbor matching algorithm. The analysis of the simulation results demonstrates that the average relative error between the reconstructed fingerprint database by the DSR-FP algorithm and the original fingerprint database is 1.21%, indicating that the accuracy of the reconstruction fingerprint database is high, and the influence of the location error can be ignored. The positioning error of the DSR-FP algorithm is less than 0.31 m. Furthermore, at the same signal-to-noise ratio, the positioning error of the DSR-FP algorithm is lesser than that of the traditional fingerprint matching algorithm, while its positioning accuracy is higher.
Keywords
Dynamic Base Station Matching Strategy; FPCA Reconstruction Algorithm; KNN Matching Algorithm; 5G Ultra-Dense Network;
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1 Y. Zhang, L. Deng, and Z. Yang, "Indoor positioning based on FM radio signals strength," in Proc. of 2017 the First International Conference on Electronics Instrumentation & Information systmes(EIIS), pp. 1-5, 2018.
2 A. Hameed and H. A. Ahmed, "Survey on indoor positioning applications based on different technologies," in Proc. of 2018 12th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics(MACS), pp. 1-5, 2018.
3 J. L. F. Ang, W. K. Lee, and B. Y. Ooi, "GreyZone: A Novel Method for Measuring and Comparing Various Indoor Positioning Systems," in Proc. of 2019 International Conference on Green and Human Information Techonolgoy, pp. 30-35, 2019.
4 S. Wang, Y. Zhang, M. Waring and L. J. Lo, "Statistical analysis of wind data using Weibull distribution for natural ventilation estimation," Science and Technology for the Build Environment, vol. 24, no. 9, pp. 922-932, Feb. 2018.   DOI
5 A. I. Aravanis, O. Munoz, A. Pascual-Iserte, and M. Di. Renzo, "On the Coordination of Base Stations in Ultra Dense Cellular Networks," in Proc. of 2019 IEEE 89th Vehicular Technology Conference, pp. 1-6, 2019.
6 Y. Wang, S. Ma, and C. L. P. Chen, "TOA-Based Passive Localization in Quasi-Synchronous Networks," IEEE Communications Letters, vol. 18, no. 4, pp. 592-595, 2014.   DOI
7 T. Qiao, Y. Zhang, and H. Liu, "Nonlinear Expectation Maximization Estimator for TDOA Localization," IEEE Wireless Communications Letters, vol. 3, no. 6, pp. 637-640, 2014.   DOI
8 H. Li and L. Chen, "RSSI-Aware Energy Saving for Large File Downloading on Smartphones," IEEE Embedded Systems letters, vol. 7, no. 2, pp. 63-66, 2015.   DOI
9 N. Bnilam, E. Tanghe, J. Steckel, W. Joseph, and M. Weyn, "ANGLE: ANGular Location Estimation Algorithms," IEEE Access, vol. 8, pp. 14620-14629, 2020.   DOI
10 Q. Chen and B. Wang, "FinCCM: Fingerprint Crowdsourcing, Clustering and Matching for Indoor Subarea Localization," IEEE Wireless Communications Letters, vol. 4, no. 6, pp. 677-680, 2015.   DOI
11 J. Zhang and G. Wang, "Enhanced Semidefinite Relaxation Method for TDOA/FDOA-Based Source Localization in Wireless Sensor Networks," Chinese Journal of Sensors and Actuators, vol. 31, no. 12, pp. 1912-1918, 2018.
12 H. Elsawy, W. Dai, M. Alouini, and M. Z. Win, "Base Station Ordering for Emergency Call Localization in Ultra-Dense Cellular Networks," IEEE Access, vol. 6, pp. 301-315, 2018.   DOI
13 J. Talvitie, M. Renfors, M. Valkama, and E. S. Lohan, "Method and Analysis of Spectrally Compressed Radio Images for Mobile-Centric Indoor Localization," IEEE Transactions on Mobile Computing, vol. 17, no. 4, pp. 845-858, 2018.   DOI
14 G. Yang, L. Zhao, Y. Dai, and Y. Xu, "A KFL-TOA UWB indoor positioning method for complex environment," in Proc. of 2017 Chinese Automation Congress(CAC), pp. 3010-3014, 2017.
15 Y. Jia, H. Tian, S. Fan, and B. Liu, "Motion Feature and Millimeter Wave Multi-path AoA-ToA Based 3D Indoor Positioning," in Proc. of 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications(PIMRC), pp. 1-7, 2018.
16 S. Zhang, P. Du, C. Chen, W. D. Zhong, and A. Alphones , "Robust 3D Indoor VLP System Based on ANN Using Hybrid RSS/PDOA," IEEE Access, vol. 7, pp. 47769-47780, 2019.   DOI
17 P. Bahl and V. N. Padmanabhan, "RADA: an in-building RF-based user location and tracking system," in Proc. of 19th Joint Conference of IEEE Computer & Communications Societies, vol. 2, no. 4, pp. 592-595, 2000. "
18 S. Horsmanheimo, S. Lembo, L. Tuomimaki, S. Huilla, P. Honkamaa, M. Laukkan, and P. Kemppi, "Indoor Positioning Platform to Support 5G Location Based Services," in Proc. of 2019 IEEE International Conference on Communications Workshops, pp. 1-6, 2019.
19 Z. Liu, X. Luo, and T. He, "Indoor positioning system based on the improved W-KNN algorithm," in Proc. of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, pp. 1355-1359, 2017.
20 F. Shen, "Research on Direction-of-Arrival Estimation in Array Signal Processing Based on Matrix Completion," M.S. thesis, Dept. of Electronic Engineering, XIDIAN University, Xi'an, China, 2017.
21 E. J. Candès and B. Recht, "Exact matrix completion via convex optimization," Foundations of Computational Mathematics, vol. 9, no. 6, pp. 717-772, 2009.   DOI
22 B. Recht, M. Fazel, and P. A. Parrilo, "Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization," SIAM Review, vol. 52, no. 3, pp. 471-501, 2010.   DOI
23 S. Negahban and M. J. Wainwright, "Restricted strong convexity and weighted matrix completion: optimal bounds with noise," Journal of Machine Learning Research, vol. 13, pp. 1665-1697, 2012.
24 E. J. Candès and T. Tao, "The power of convex relaxation: Near-optimal matrix completion," IEEE Transactions on Information Theory, vol. 56, no. 5, pp. 2053-2080, 2010.   DOI