다양한 도심 환경에 따른 ZSM 알고리즘의 성능 분석
Performance Analysis of Zonotope Shadow Matching Algorithm According to Various Urban Environments
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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.
Abstract
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.
참고문헌 (14)
- Althoff, M., "Computing reachable sets of hybrid systems using a combination of zonotopes and polytopes", Nonlinear analysis : hybrid systems, 4, pp.233~249 (2010)[DOI]
- Bhamidipati, S., "Set-valued shadow matching using zonotopes for 3D-map-aided GNSS localization", Navigation: Journal of the Institute of Navigation, 69 (2022)[DOI]
- Groves, P. D, "Shadow matching : A new GNSS positioning technique for urban canyons", The Journal of Navigation, 64, pp.417~430 (2011)[DOI]
- Groves, P. D., "Performance assessment of 3D-mapping-aided GNSS part 1 : Algorithms, user equipment, and review", Navigation, 66, pp.341~362 (2019)[DOI]
- Groves, P. D., "GNSS shadow matching: The challenges ahead", Proc. 28th International Technical Meeting of the Satellite Division of The Institute of Navigation, pp.2421~2443 (2015)
- Jia, M., "Ground vehicle navigation integrity monitoring for multi-constellation GNSS fused with cellular signals of opportunity", Proc. ITSC, pp.3978~3983 (2021)
- Kim, S., "Machine learning-based classification of GPS signal reception conditions using a dual polarized antenna in urban areas", Proc. IEEE, 2023, 113~118
- Kim, S., "Single antenna based GPS signal reception condition classification using machine learning approaches", Journal of Positioning, Navigation, and Timing, 12, pp.149~155 (2023)[DOI]
- Lee, H., "Optimal parameter inflation to enhance the availability of single-frequency GBAS for intelligent air transportation", IEEE Transactions on Intelligent Transportation Systems, 23, pp.17801~17807 (2022)[DOI]
- Lee, H., "Urban road safety prediction : A satellite navigation perspective", IEEE Intelligent Transportation Systems Magazine, 14, pp.94~106 (2022)[DOI]
- Martens, H., "Pre-whitening of data by covariance-weighted pre-processing", Journal of Chemometrics, 17, pp.153~165 (2003)[DOI]
- "ONEGEO"[URL]
- Wang, L., "GNSS shadow matching: Improving urban positioning accuracy using a 3D city model with optimized visibility scoring scheme", Navigation, 60, pp.195~207 (2013)[DOI]
- Yun, J., "Vehicle reference dynamics estimation by speed and heading information sensed from a distant point", Journal of Positioning, Navigation, and Timing, 11, pp.209~215 (2022)[DOI]