GNSS / INS integrated navigation system has been widely used. However, the loosely coupled system will degrade when the GNSS system is subject to environmental constraints. Meanwhile, the tightly coupled system of GNSS / INS will significantly decrease the positioning accuracy due to the frequent changes in constellation geometry layout. To solve this problem, the adaptive Kalman filter tight integrated navigation method based on new information is used in this paper to design a vehicle-mounted experiment scheme in an urban environment. It takes the errors of the pseudo-range and pseudo-range rate as the observation quantity to perform filtering estimation. At the same time, the adaptive factor is determined according to the innovation variance’s theoretical and actual values, and the system’s measurement noise is adjusted to correct the system errors further. Experimental results show that when the number of visible satellites is less than four, the 3D positioning error of this method is reduced by about 24. 2% compared with the traditional Kalman filtering method. In the environment of frequent changes in visible satellite conditions, the maximum positioning error is reduced by about 60%. The results showed that this method could adjust the observation noise of satellite pseudo-distance when the observation conditions are poor, reduce the state error, enhance the robustness of the filter, and provide more accurate position information.