Single Antenna Based GPS Signal Reception Condition Classification Using Machine Learning Approaches
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초록
In urban areas it can be difficult to utilize global navigation satellite systems (GNSS) due to signal reflections and blockages. It is thus crucial to detect reflected or blocked signals because they lead to significant degradation of GNSS positioning accuracy. In a previous study, a classifier for global positioning system (GPS) signal reception conditions was developed using three features and the support vector machine (SVM) algorithm. However, this classifier had limitations in its classification performance. Therefore, in this study, we developed an improved machine learning based method of classifying GPS signal reception conditions by including an additional feature with the existing features. Furthermore, we applied various machine learning classification algorithms. As a result, when tested with datasets collected in different environments than the training environment, the classification accuracy improved by nine percentage points compared to the existing method, reaching up to 58%.
Abstract
In urban areas it can be difficult to utilize global navigation satellite systems (GNSS) due to signal reflections and blockages. It is thus crucial to detect reflected or blocked signals because they lead to significant degradation of GNSS positioning accuracy. In a previous study, a classifier for global positioning system (GPS) signal reception conditions was developed using three features and the support vector machine (SVM) algorithm. However, this classifier had limitations in its classification performance. Therefore, in this study, we developed an improved machine learning based method of classifying GPS signal reception conditions by including an additional feature with the existing features. Furthermore, we applied various machine learning classification algorithms. As a result, when tested with datasets collected in different environments than the training environment, the classification accuracy improved by nine percentage points compared to the existing method, reaching up to 58%.
참고문헌 (32)
- Xiwei Bai, "Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons", IET Intelligent Transport Systems, 14(8), pp.908~914 (2020), Institution of Engineering and Technology (IET)[DOI]
- James C. Bezdek, "Generalized k-nearest neighbor rules", Fuzzy Sets and Systems, 18(3), pp.237~256 (1986), Elsevier BV[DOI]
- Flavia Causa, "Improving Navigation in GNSS-Challenging Environments: Multi-UAS Cooperation and Generalized Dilution of Precision", IEEE Transactions on Aerospace and Electronic Systems, 57(3), pp.1462~1479 (2021), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Pau Closas, "A Statistical Multipath Detector for Antenna Array Based GNSS Receivers", IEEE Transactions on Wireless Communications, 10(3), pp.916~929 (2011), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Friedman, J. H., "Greedy function approximation: A gradient boosting machine", The Annals of statistics, 29, pp.1189~1232 (2001)
- Groves, P. D., "Novel multipath mitigation methods using a dual-polarization antenna", Proc. ION GNSS, pp.140~151 (2010)
- Hsu, L. -T., "GNSS multipath detection using a machine learning approach", Proc. IEEE ITSC, pp.1~6 (2017)
- 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 GPS multipath detection method using dual antennas", Proc. ASCC, pp.691~695 (2022)
- Kim, S., "GPS multipath detection based on carrier-to-noise-density ratio measurements from a dual-polarized antenna", Proc. ICCAS, pp.1099~1103 (2021)
- Kim, S., "Motion planning by reinforcement learning for an unmanned aerial vehicle in virtual open space with static obstacles", Proc. ICCAS, pp.784~787 (2020)
- Kim, S., "Support vector machine-based GPS multipath detection method", Proc. IPNT Conference, pp.365~368 (2022)
- Kim, W., "First demonstration of the Korean eLoran accuracy in a narrow waterway using improved ASF maps", IEEE Transactions on Aerospace and Electronic Systems, 58, pp.1492~1496 (2022)[DOI]
- Kouiroukidis, N., "The effects of dimensionality curse in high dimensional kNN search", Proc. Panhellenic Conference on Informatics, pp.41~45 (2011)
- Nobuaki Kubo, "GNSS Multipath Detection Using Continuous Time-Series C", N0, 2020, pp.20(14), 1~25[DOI]
- Halim Lee, "Optimal Parameter Inflation to Enhance the Availability of Single-Frequency GBAS for Intelligent Air Transportation", IEEE Transactions on Intelligent Transportation Systems, 23(10), pp.17801~17808 (2022), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Lee, H., "A preliminary study of machinelearning-based ranging with LTE channel impulse response in multipath environment", Proc. ICCEAsia, pp.1~4 (2020)
- Halim Lee, "Urban Road Safety Prediction: A Satellite Navigation Perspective", IEEE Intelligent Transportation Systems Magazine, 14(6), pp.94~106 (2022), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Liu, Y., "New machine learning algorithm: Random forest", Proc. ICICA, pp.246~252 (2012)
- MacGougan G., "Performance analysis of a stand-alone high-sensitivity receiver", GPS Solutions, 6(3), pp.179~195 (2002), Springer Science and Business Media LLC[DOI]
- Misra, P., "The Global Positioning System:Signals, Measurements, and Performance" (2001), Ganga-Jamuna Press
- Miura, S., "GPS multipath detection and rectification using 3D maps", Proc. IEEE ITSC, pp.1~7 (2013)
- S.R. Safavian, "A survey of decision tree classifier methodology", IEEE Transactions on Systems, Man, and Cybernetics, 21(3), pp.660~674 (1991), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Nan Shen, "Site-specific real-time GPS multipath mitigation based on coordinate time series window matching", GPS Solutions, 24(3), pp.1~14 (2020), Springer Science and Business Media LLC[DOI]
- Philip R. R. Strode, "GNSS multipath detection using three-frequency signal-to-noise measurements", GPS Solutions, 20(3), pp.399~412 (2015), Springer Science and Business Media LLC[DOI]
- Rui Sun, "GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System", Journal of Navigation, 72(3), pp.685~701 (2018), Cambridge University Press (CUP)[DOI]
- Rui Sun, "A gradient boosting decision tree based GPS signal reception classification algorithm", Applied Soft Computing, 86, pp.105942~ (2020), Elsevier BV[DOI]
- Suzuki, T., "NLOS multipath detection by using machine learning in urban environments", Proc. ION GNSS+2017, pp.3958~3967 (2017)
- Wang, L., "Multi-constellation GNSS performance evaluation for urban canyons using large virtual reality city models", The Journal of Navigation, 65, pp.459~476 (2012)[DOI]
- Xu, L., "NLOS and multipath detection using doppler shift measurements", Proc. ION GNSS+2019, pp.4064~4075 (2019)
- Hyojung Yoon, "An Online SBAS Service to Improve Drone Navigation Performance in High-Elevation Masked Areas", Sensors, 20(11), pp.1~20 (2020), MDPI AG[DOI]
- Ni Zhu, "GNSS Position Integrity in Urban Environments: A Review of Literature", IEEE Transactions on Intelligent Transportation Systems, 19(9), pp.2762~2778 (2018), Institute of Electrical and Electronics Engineers (IEEE)[DOI]