Enhanced Vehicle Positioning Through GNSS/INS Integration With INS-Aided Modified Partial Ambiguity Resolution Method

The fusion of the Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) presents a leading solution to mitigate GNSS positioning limitations in obstructed environments. Achieving high-precision positioning, whether through independent GNSS or GNSS/INS integration, is significantly impacted by reliable Ambiguity Resolution (AR). This paper introduces enhancements in two crucial AR stages: obtaining float solutions for ambiguities and ambiguity searching. High-precision float solutions necessitate precise prior position information, constrained by GNSS pseudorange accuracy. This study investigates the utilization of an error model derived from the high-precision Tightly Coupled (TC) integration positioning solution to rectify the INS. The corrected INS position replaces the pseud-orange position as prior information for acquiring float solutions. In the ambiguity search process, a Modified Partial AR (MPAR) method, is introduced. Through a specific methodology, an optimal subset of satellite ambiguities with superior performance is selected. The fixed ambiguity solutions provided by this subset significantly aid in resolving nonoptimal subsets, thereby increasing the number of satellites for which ambiguity fixing is successful. The proposed Corrected INS-Aided MPAR (CIA-MPAR) method notably enhances AR success rates by leveraging fixed integer ambiguities from an optimal subset to assist non-optimal subsets. Experimental results demonstrate that the proposed CIA-MPAR method, when applied to the GNSS/INS integration, consistently outperforms other AR methods across all evaluated aspects.