Abstract
The key requirement of centimeter-level real-time kinematic (RTK) positioning using the Global Positioning System (GPS) relies on the ability to fast and accurately determine the ambiguities of carrier-phase observations to their inherent integer values. In addition, the identification must be completed on the fly since the remote receiver is constantly in motion. The Kalman filter-based algorithm described in this paper uses an ionospheric information filter to perform on-the-fly phase ambiguity resolution for high precision RTK applications. Experiments based on 16 independent test baselines ranging from 10-50 km in length indicate that the algorithm can reliably achieve centimeter-level positioning accuracy, provided that a small enough threshold value for ambiguity identification is pre-defined and that sufficient geometry change in the GPS constellation is observed. Experimental results also show that the convergence (initialization) time for ambiguity resolution is linearly proportional to instantaneous baseline length, and the slope of the regression line increases with tighter ambiguity identification criteria.
