Journal of Positioning, Navigation, and Timing

  • 제13권3호
  • /
  • Pages.215-220
  • /
  • 2024
  • /
  • 2288-8187(pISSN)
  • /
  • 2289-0866(eISSN)
항법시스템학회 (The Institute of Positioning, Navigation, and Timing)
권호 표지
DOI QR코드

DOI QR Code

다양한 도심 환경에 따른 ZSM 알고리즘의 성능 분석

Performance Analysis of Zonotope Shadow Matching Algorithm According to Various Urban Environments

  • 김상현 ;
  • 서지원
  • Sanghyun Kim (School of Integrated Technology, Yonsei University) ;
  • Jiwon Seo (School of Integrated Technology, Yonsei University)
  • 투고 : 2024.05.13
  • 심사 : 2024.06.13
  • 발행 : 2024.09.15
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In urban areas, signals can be blocked and reflected by buildings, reducing the reliability of global navigation satellite systems (GNSS). To address this, the zonotope shadow matching (ZSM) algorithm has been proposed to estimate the set-valued receiver position by calculating the GNSS shadow based on the zonotope. However, the existing study only analyzed the performance of ZSM in dense urban areas where GNSS shadows occur frequently, and the performance analysis in various urban environments was insufficient. Therefore, in this paper, we analyzed the performance of the ZSM algorithm in four urban environments with different characteristics. The results showed that the receiver position estimation performance of ZSM was relatively poor in environments where buildings were not densely populated, and the performance of ZSM was shown to be effective in urban environments with narrow roads and tall buildings.

키워드

과제정보

본 연구는 과학기술정보통신부의 재원으로 한국연구재단, 무인이동체원천기술개발사업단의 지원을 받아 수행되었음(2020M3C1C1A01086407). 또한, 이 연구는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(RS-2024-00358298).

참고문헌

  1. Althoff, M., Stursberg, O., & Buss, M. 2010, Computing reachable sets of hybrid systems using a combination of zonotopes and polytopes, Nonlinear analysis: hybrid systems, 4, 233-249. https://doi.org/10.1016/j.nahs.2009.03.009
  2. Bhamidipati, S., Kousik, S., & Gao, G. 2022, Set-valued shadow matching using zonotopes for 3D-map-aided GNSS localization, Navigation: Journal of the Institute of Navigation, 69, navi.547. https://doi.org/10.33012/navi.547
  3. Groves, P. D. 2011, Shadow matching: A new GNSS positioning technique for urban canyons, The Journal of Navigation, 64, 417-430. https://doi.org/10.1017/S0373463311000087
  4. Groves, P. D. & Adjrad, M. 2019, Performance assessment of 3D-mapping-aided GNSS part 1: Algorithms, user equipment, and review, Navigation, 66, 341-362. https://doi.org/10.1002/navi.288
  5. Groves, P. D., Wang, L., Adjrad, M., & Ellul, C. 2015, GNSS shadow matching: The challenges ahead, in Proc. 28th International Technical Meeting of the Satellite Division of The Institute of Navigation, 14-18 Sept 2015, Tampa, FL, USA, pp.2421-2443. https://www.ion.org/publications/abstract.cfm?articleID=12866
  6. Jia, M., Lee, H., Khalife, J., Kassas, Z. M., & Seo, J. 2021, Ground vehicle navigation integrity monitoring for multi-constellation GNSS fused with cellular signals of opportunity, in Proc. ITSC, 19-22 Sept 2021, Indianapolis, IN, USA, pp.3978-3983. https://doi.org/10.1109/ITSC48978.2021.9564686
  7. Kim, S. & Seo, J. 2023, Machine learning-based classification of GPS signal reception conditions using a dual polarized antenna in urban areas, in Proc. IEEE/ION Position, Location, and Navigation Symposium, 24-27 Apr 2023, Monterey, CA, USA, pp.113-118. https://doi.org/10.1109/PLANS53410.2023.10140036
  8. Kim, S., Park, S., & Seo, J. 2023, Single antenna based GPS signal reception condition classification using machine learning approaches, Journal of Positioning, Navigation, and Timing, 12, 149-155. https://doi.org/10.11003/JPNT.2023.12.2.149
  9. Lee, H., Pullen, S., Lee, J., Park, B., Yoon, M., et al. 2022a, Optimal parameter inflation to enhance the availability of single-frequency GBAS for intelligent air transportation, IEEE Transactions on Intelligent Transportation Systems, 23, 17801-17807. https://doi.org/10.1109/TITS.2022.3157138
  10. Lee, H., Seo, J., & Kassas, Z. 2022b, Urban road safety prediction: A satellite navigation perspective, IEEE Intelligent Transportation Systems Magazine, 14, 94-106. https://doi.org/10.1109/MITS.2022.3181557
  11. Martens, H., Hoy, M., Wise, B. M., Bro, R., & Brockhoff, P. B. 2003, Pre-whitening of data by covariance-weighted pre-processing, Journal of Chemometrics, 17, 153-165. https://doi.org/10.1002/cem.780
  12. ONEGEO [Internet], cited 2024 May 7, available from: https://onegeo.co
  13. Wang, L., Groves, P. D., & Ziebart, M. K. 2013, GNSS shadow matching: Improving urban positioning accuracy using a 3D city model with optimized visibility scoring scheme, Navigation, 60, 195-207. https://doi.org/10.1002/navi.38
  14. Yun, J., Kim, G., Cho, M., Park, B., Seo, H. et al. 2022, Vehicle reference dynamics estimation by speed and heading information sensed from a distant point, Journal of Positioning, Navigation, and Timing, 11, 209-215. https://doi.org/10.11003/JPNT.2022.11.3.209