Abstract Aiming at the problem of susceptibility to satellite signals in GNSS vehicle navigation in urban environments, we use the GNSS/INS combined algorithm to improve positioning performance of urban vehicles in complex environments. Based on actual measurement GNSS data from the urban environment, we evaluate and analyze the positioning results, and use the conventional Kalman filtering algorithm of the GNSS/INS combination to realize the navigation of the satellite lock-out area. At the same time, we propose an adaptive Kalman filtering algorithm based on innovation, which can effectively enhance the navigation and positioning capabilities of vehicles in areas with fewer satellites and severe signal interference. This method uses the relationship between measurement and prediction to construct an adaptive factor to improve positioning accuracy. The results show that the conventional Kalman filter can guarantee sub-meter navigation accuracy when the satellite signal is out of lock in 20 s. For satellite signals with severely interference, the positioning accuracy of the adaptive Kalman filtering algorithm is increased by 30% when compared with the conventional Kalman filter. The adaptive Kalman filtering algorithm can meet the needs of high-precision and high-reliability vehicle navigation and positioning services in the complex urban environment.
WANG Fu,HAN Baomin,HU Liangliang et al. Research on GNSS/INS Integrated Navigation Algorithm in Complex Urban Environments[J]. jgg, 2022, 42(1): 15-20.
WANG Fu,HAN Baomin,HU Liangliang et al. Research on GNSS/INS Integrated Navigation Algorithm in Complex Urban Environments[J]. jgg, 2022, 42(1): 15-20.