지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
Recently, owing to the development of autonomous vehicles, research on precisely determining the position of a moving object has been actively conducted. Previous research mainly used the fusion of GNSS/IMU (Global Positioning System / Inertial Navigation System) and sensors attached to the vehicle through a Kalman filter. However, in recent years, new technologies have been used to determine the location of a moving object owing to the improvement in computing power and the advent of deep learning. Various techniques using RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and NARX (Nonlinear Auto-Regressive eXogenous model) exist for such learning-based positioning methods.
The purpose of this study is to compare the precision of existing filter-based sensor fusion technology and the NARX-based method in case of GNSS signal blockages using simulation data. When the filter-based sensor integration technology was used, an average horizontal position error of 112.8 m occurred during 60 seconds of GNSS signal outages. The same experiment was performed 100 times using the NARX. Among them, an improvement in precision was confirmed in approximately 20% of the experimental results. The horizontal position accuracy was 22.65 m, which was confirmed to be better than that of the filter-based fusion technique.
The purpose of this study is to compare the precision of existing filter-based sensor fusion technology and the NARX-based method in case of GNSS signal blockages using simulation data. When the filter-based sensor integration technology was used, an average horizontal position error of 112.8 m occurred during 60 seconds of GNSS signal outages. The same experiment was performed 100 times using the NARX. Among them, an improvement in precision was confirmed in approximately 20% of the experimental results. The horizontal position accuracy was 22.65 m, which was confirmed to be better than that of the filter-based fusion technique.
목차
Abstract
1. Introduction
2. Methodology
3. Performance Evaluation
4. Summary and Conclusion
References
참고문헌 (6)
참고문헌 신청
Al-Bistar, N., and Gavrilov, A. (2021), A New Method for Compensating the Errors of Integrated Navigation Systems Using Artificial Neural Networks. Measurement, Vol. 168, pp. 1-12.
Aslinezhad, M., Malekijavan, A., and Abbasi, P. (2020), ANN-assisted Robust GPS/INS Information Fusion to Bridge GPS Outage, EURASIP Journal on Wireless Communications and Networking, Vol. 2020, No. 1, pp. 1-18.
Baker, T. and Åkerblad, M. (2018), A Study on Long Short-term Memory Networks Applied to Local Positioning, Master’s thesis, Royal Institute of Technology, Stockholm, Sweden, 77p.
Dai, H.F., Bian, H.W., Wang, R.Y., & Ma, H. (2020), An INS/GNSS Integrated Navigation in GNSS Denied Environment using Recurrent Neural Network. Defence technology, Vol. 16, No. 2, pp. 334-340.
Lee, Y., and Kwon J.H. (2018), Performance Analysis of the GNSS/MEMS-IMU/on-Board Vehicle sensor/ magnetometer-based Positioning System during GNSS Signal Blockage, International Journal of Urban Sciences , Vol. 23, No. 3, pp. 1-10.
Aslinezhad, M., Malekijavan, A., and Abbasi, P. (2020), ANN-assisted Robust GPS/INS Information Fusion to Bridge GPS Outage, EURASIP Journal on Wireless Communications and Networking, Vol. 2020, No. 1, pp. 1-18.
Baker, T. and Åkerblad, M. (2018), A Study on Long Short-term Memory Networks Applied to Local Positioning, Master’s thesis, Royal Institute of Technology, Stockholm, Sweden, 77p.
Dai, H.F., Bian, H.W., Wang, R.Y., & Ma, H. (2020), An INS/GNSS Integrated Navigation in GNSS Denied Environment using Recurrent Neural Network. Defence technology, Vol. 16, No. 2, pp. 334-340.
Lee, Y., and Kwon J.H. (2018), Performance Analysis of the GNSS/MEMS-IMU/on-Board Vehicle sensor/ magnetometer-based Positioning System during GNSS Signal Blockage, International Journal of Urban Sciences , Vol. 23, No. 3, pp. 1-10.
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