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http://dx.doi.org/10.6109/jkiice.2022.26.6.936

Multi-directional DRSS Technique for Indoor Vehicle Navigation  

Kim, Seon (Department of Radio and Information Communications Engineering, Chungnam National University)
Park, Pangun (Department of Radio and Information Communications Engineering, Chungnam National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.6, 2022 , pp. 936-942 More about this Journal
Abstract
While indoor vehicle navigation is an essential component in large-scale parking garages of major cities, technical limitations and challenging propagation environments considerably degrade the accuracy of existing localization techniques. This paper proposes a proximity detection scheme using low-cost beacons where a handheld mobile device within a moving vehicle autonomously detects its approximate position and moving direction by only observing Received Signal Strength (RSS) values of beacon signals. The proposed approach essentially exploits the differential RSS technique of multi-directional beams to reduce the impact of the environment, vehicle, and mobile device. A low-cost multi-directional beacon prototype is developed using Bluetooth technology. The localization performance is evaluated using 96 beacons in an underground parking garage within an area of 394.8m×304.3m. Experimental results show that the 90th percentile of the average proximity detection error is 0.8m. Furthermore, our proposed scheme provides robust proximity detection performance with various vehicles and mobile devices.
Keywords
Directional beam; Differential RSS; Proximity detection; Localization; Indoor vehicle navigation;
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1 A. Mikov, A. Panyov, V. Kosyanchuk, and I. Prikhodko, "Sensor Fusion For Land Vehicle Localization Using Inertial MEMS and Odometry," in IEEE International Symposium on Inertial Sensors and Systems, Naples: FL, USA, pp. 1-2, Apr. 2019.
2 R. Faragher and R. Harle, "Location Fingerprinting With Bluetooth Low Energy Beacons," IEEE Journal on Selected Areas in Communications, vol. 33, no. 11, pp. 2418-2428, May. 2015.   DOI
3 F. Zafari, A. Gkelias, and K. K. Leung, "A Survey of Indoor Localization Systems and Technologies," IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2568-2599, Apr. 2019.   DOI
4 X. Zhu, W. Qu, T. Qiu, L. Zhao, M. Atiquzzaman, and D. O. Wu, "Indoor Intelligent Fingerprint-based Localization: Principles, Approaches and Challenges," IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2634-2657, Aug. 2020.   DOI
5 S. Kuutti, S. Fallah, K. Katsaros, M. Dianati, F. Mccullough, and A. Mouzakitis, "A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications," IEEE Internet of Things Journal, vol. 5, no. 2, pp. 829-846, Apr. 2018.   DOI
6 T. Sutjarittham, H. H. Gharakheili, S. S. Kanhere, and V. Sivaraman, "Monetizing Parking IoT Data via Demand Prediction and Optimal Space Sharing," IEEE Internet of Things Journal, vol. 9, no. 8, pp. 5629-5644, Apr. 2022.   DOI
7 P. Park, P. D. Marco, J. Nah, and C. Fischione, "Wireless Avionics Intracommunications: A Survey of Benefits, Challenges, and Solutions," IEEE Internet of Things Journal, vol. 8, no. 10, pp. 7745-7767, May. 2021.   DOI
8 F. Yin and F. Gunnarsson, "Distributed Recursive Gaussian Processes for RSS Map Applied to Target Tracking," IEEE Journal of Selected Topics in Signal Processing, vol. 11, no. 3, pp. 492-503, Apr. 2017.   DOI
9 C. Zhou, J. Liu, M. Sheng, Y. Zheng, and J. Li, "Exploiting Fingerprint Correlation for Fingerprint-Based Indoor Localization: A Deep Learning Based Approach," IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 5762-5774, Jun. 2021.   DOI
10 D. Sun, E. Wei, L. Yang, and S. Xu, "Improving Fingerprint Indoor Localization Using Convolutional Neural Networks," IEEE Access, vol. 8, pp. 193396-193411, Oct. 2020.   DOI
11 B. Jang and H. Kim, "Indoor Positioning Technologies Without Offline Fingerprinting MAP: A Survey," IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 508-525, Aug. 2019.   DOI
12 J. Li, I. Lu, and J. Lu, "Cramer-Rao Lower Bound Analysis of Data Fusion for Fingerprinting Localization in Non-Line-of-Sight Environments," IEEE Access, vol. 9, pp. 18607-18624, Jan. 2021.   DOI