A New Approach for Computationally Efficient and Reliable Carrier Integer Ambiguity Resolution in GPS/INS
This dissertation considers reliable real-time navigation with Real-Time Kinematic (RTK) GPS aided Inertial Navigation System (INS). To improve the accuracy and reliability of the aided INS, a Contemplative Real-Time framework (CRT) is proposed by combining the conventional Kalman Filtering and the Bayesian smoothing which considers all the navigation information over a time window. To facilitate the formulation and the solution of the CRT problem, a probabilistic graphical model called Factor Graph is utilized.
To enhance the robustness of the navigation system to faulty measurements, a novel robust graph optimization method referred as Hypothesis Test based Least Soft-thresholding Square (HT-LSS) is proposed. Due to the integer ambiguity inherent in the RTK GPS carrier phase measurements, in this work the classical Factor Graph modeling is extended for RTK GPS/INS applications by incorporating integer unknowns. Nonlinear Mixed Integer Least Square (NMILS) is required to solve the CRT RTK GPS/INS problem. The major contribution of this thesis is the proposition of a novel Common-Position-Shift method to reduce the computational cost of the NMILS in this problem. In addition, a robust real-time differential correction computation approach was developed for reliable DGPS/RTK applications with internet transported differential information in RTCM and Ntrip standard. The proposed algorithms are evaluate using data acquired with the sensor platform mounted on an automotive vehicle to illustrate the performance.