MPARELAM: A Robust Approach for Ambiguity Resolution in Complex RTK Positioning Scenarios

Real-time kinematic (RTK) positioning technology utilizes carrier phase measurements for receiver positioning and requires resolving integer carrier phase ambiguities to achieve high-precision results. This process, known as ambiguity resolution (AR), traditionally relies on the classical least-squares ambiguity decorrelation adjustment (LAMBDA) algorithm and partial ambiguity resolution (PAR) strategy. However, these established methods are limited in processing high-dimensional data across multiple frequencies and systems. Therefore, this study aims to investigate the challenges posed by these methods in such scenarios and propose an alternative approach. The proposed method, called MPARELAM, integrates the enhanced LAMBDA (ELAMBDA) algorithm with a modified PAR (MPAR) strategy, combining advanced search capabilities and robust AR. The quantitative evaluation of the MPARELAM method considers the success rate and the total time of AR, as well as the average standard deviation of the positioning results. The results reveal that the proposed method significantly enhances AR accuracy and reduces positioning errors in carrier-phase-based RTK positioning.