Abstract
When UAVs fly in cities, the environment often interfered with GPS signals, reducing accuracy. The changes in statistical noise characteristics caused by abnormal measurement information can quickly decrease the accuracy of integrated navigation filtering or even divergence, affecting UAVs’ stability and safety. In this paper, a combined navigation model of GNSS/VG dual observation is constructed, combined with an adaptive filtering algorithm based on fading factor, to improve the navigation accuracy and attitude angle stability in the case of abnormal measurement information. The simulation and sports car experiments show that, compared with the ESKF(Error State Kalman Filter) algorithm, the SHAESKF(Sage-Husa Adaptive Error State Kalman Filter) algorithm improves navigation system performance and enhances stability.
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Dong, S., Guo, Z., Wu, L., Deng, Z. (2023). Adaptive Filtering Algorithm for Urban Traffic UAV Integrated Navigation Based on MEMS Devices. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_52
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DOI: https://doi.org/10.1007/978-981-19-6613-2_52
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