Journal of Positioning, Navigation, and Timing

  • 제13권3호
  • /
  • Pages.331-339
  • /
  • 2024
  • /
  • 2288-8187(pISSN)
  • /
  • 2289-0866(eISSN)
항법시스템학회 (The Institute of Positioning, Navigation, and Timing)
권호 표지
DOI QR코드

DOI QR Code

시뮬레이션을 이용한 누적 RSSI 신호 기반의 항법 기술 성능 분석

Analysis of Localization Technology Performance Based on Accumulated RSSI Signal Using Simulation

  • 신범주 ;
  • 이택진
  • Beomju Shin (Division of Software, Hallym University) ;
  • Taikjin Lee (Augmented Safety System with Intelligence Sensing & Tracking, Korea Institute of Science and Technology)
  • 투고 : 2024.06.14
  • 심사 : 2024.07.26
  • 발행 : 2024.09.15
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Reliable and precise indoor localization is crucial for personal navigation, emergency rescue, and monitoring workers indoors. To use this technology in different applications, it is important to make it less dependent on infrastructure and to keep the error as small as possible. Fingerprinting stands out as a popular choice for indoor positioning because it leverages existing infrastructure and works with just a smartphone. However, its accuracy heavily relies on the quality of that infrastructure. For instance, having too few access points or beacons can greatly reduce its effectiveness. To reduce dependence on RF infrastructure, we have developed surface correlation (SC) using accumulated Received Signal Strength Indicator (RSSI) signals This approach constructs a user mask for radio map comparisons using an accumulated RSSI vector and the trajectory of the user, which is estimated through PDR. The location with the highest correlation is considered as the user's position after comparison. Through a simulation, the performance of short RSSI vector-based technology and SC is analyzed, and future directions for the development of SC are discussed.

키워드

참고문헌

  1. Bahl, P. & Padmanabhan, V. N. 2000, RADAR: An In-Building RF-based User Location and Tracking System, in Proc. IEEE INFOCOM Conf. Comput. Commun. 19th Annu. Joint Conf. IEEE Comput. Commun. Societies, 26-30 Mar. 2000, Tel Aviv, Israel, pp.775-784. https://doi.org/10.1109/INFCOM.2000.832252
  2. Brunato, M. & Battiti, R. 2005, Statistical Learning Theory for Location Fingerprinting in Wireless LANs, Computer Networks, 47, 825-845. https://doi.org/10.1016/j.comnet.2004.09.004
  3. Guo, G., Chen, R., Niu, X., Yan, K., Xu, S., et al. 2023, Factor Graph Framework for Smartphone Indoor Localization: Integrating Data-Driven PDR and Wi-Fi RTT/RSS Ranging, IEEE Sensors Journal, 23, 12346-12354. https://doi.org/10.1109/JSEN.2023.3267121
  4. He, S. & Chan, S.-H. G. 2016, Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons, IEEE Commun. Surv. Tutor. 18, 466-490. https://doi.org/10.1109/COMST.2015.2464084
  5. Jang, J.-W. & Hong, S.-N. 2018, Indoor Localization with WiFi Fingerprinting Using Convolutional Neural Network, 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN), 03-06 July 2018, Prague, Czech Republic, pp.753-758. https://doi.org/10.1109/ICUFN.2018.8436598
  6. Lee, J. H., Shin, B., Shin, D., Park, J., Ryu, Y. S., et al. 2019, Surface correlation-based fingerprinting method using LTE signal for localization in urban canyon, Sensors 19, 3325. https://doi.org/10.3390/s19153325
  7. Li, F., Liu, M., Zhang, Y. & Shen, W. 2019, A Two-Level WiFi Fingerprint-Based Indoor Localization Method for Dangerous Area Monitoring, Sensors 19, 4243. https://doi.org/10.3390/s19194243
  8. Shang, S. & Wang, L. 2022, Overview of WiFi fingerprintingbased indoor positioning, IET Communications, 16, 725-733. https://doi.org/10.1049/cmu2.12386
  9. Shen, G., Chen, Z., Zhang, P., Moscibroda, T., & Zhang, Y. 2013, Walkie-Markie: Indoor Pathway Mapping Made Easy, Proceedings of the 10th USENIX Conference on Networked Systems Design and Implementation, Apr. 2-5, 2013, Berkeley, CA, pp.85-98. https://dl.acm.org/doi/10.5555/2482626.2482636
  10. Shin, B., Lee, J. H. & Lee, T. 2023, Novel indoor fingerprinting method based on RSS sequence matching, Mea-surement, 223, 113719. https://doi.org/10.1016/j.measurement.2023.113719
  11. Singh, N., Choe, S., & Punmiya, R. 2021, Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview, IEEE Access, 9, 127150-127174. https://doi.org/10.1109/ACCESS.2021.3111083