Optimal Measurement Projection in GNSS-RTK Factor Graph Optimization

This paper presents two optimal measurement projection schemes for the factor-graph-based Global Navigation Satellite System (GNSS) positioning with Real-Time-Kinematics (RTK). While factor graph optimization (FGO) has demonstrated improved accuracy and robustness in GNSS positioning compared to conventional filtering-based methods, the improvement has a cost of increased computational complexity due to the fact that FGO processes the batch of historical data simultaneously. Two measurement projection schemes are proposed to alleviate the computational burden of FGO by optimally projecting the GNSS measurements into a lower-dimensional subspace. Thereby, the dimensionality of the factor graph optimization is significantly reduced with only minimally performance loss. Monte Carlo simulation results demonstrate that the proposed measurement reduction schemes can achieve a significant computational speedup for the FGObased GNSS-RTK positioning while retaining high-precision positioning performance.