| Summary: | 碩士 === 國立臺灣大學 === 應用力學研究所 === 102 === In 2013, an RTK(real-time kinematic) algorithm has been developed by using differential measurement data from two GPS single frequency receivers. The integer ambiguities for double-difference observables are resolved by applying LAMBDA (Least-Square AmBiguity Decorrelation Adjustment) method. That algorithm shall be referred as Type I RTK in this research [27]. Due to the fact that the positioning accuracy and precision of Type I RTK algorithm are not satisfied, this research adopts another hierarchy which includes double-differenced integer ambiguity in the state vector in applying the Kalman filter. Such algorithm is referred to as Type II RTK in this research. However, although the positioning precision of Type II RTK has been enhanced in comparing with Type I RTK, the positioning accuracy is still not improved. To solve this problem, this research develops an adaptive Kalman filter by fuzzy logic based on the model of Type II RTK. The experimental results show that fuzzy logic RTK can improve the positioning accuracy with the same level of precision as that of Type II RTK. It is then observed that, in normal condition, type II RTK gives rise to more precise positioning result that that from using Type I RTK, while fuzzy adaptive RTK is more accurate that Type II RTK.
|
|---|
