Improving Single-Frequency Multi-GNSS RTK/INS Tightly Coupled Integration by Mixing Time-Differenced and Double-Differenced Carrier Phase for Vehicle-Borne Positioning

The integration of multiconstellation global navigation satellite system (GNSS) signals with existing GPS systems enhances integer ambiguity resolution (IAR), a technique essential for achieving continuous and high-precision positioning in real-time kinematic (RTK) systems. However, incorrect IAR can lead to position errors of several meters and reduce system availability. The tight integration of an inertial navigation system (INS) with multi-GNSS significantly improves IAR performance, benefiting from the INS-assisted IAR effect. Building on previous research in INS-assisted IAR, this study focuses on enhancing both IAR performance and computational efficiency for low-cost vehicle-borne applications in complex environments. We propose a robust and efficient two-stage filtering fusion method that combines multi-GNSS and high sampling rate IMU data. This approach integrates time-differenced carrier phase (TDCP) and double-differenced carrier phase (DDCP) measurements within a hybrid model, where TDCP-based GNSS/INS tight integration supports DDCP-based RTK. This method significantly improves IAR performance in challenging scenarios, such as environments with substantial signal interference or strong multipath effects. Moreover, the hybrid model greatly enhances computational efficiency by 45% compared to traditional multi-GNSS RTK/INS tight integration, making it more suitable for real-time applications. The effectiveness of our approach is validated through real-world experiments conducted in both open-sky and urban environments. Results show that the proposed algorithm outperforms conventional RTK/INS tight integration frameworks, achieving more robust and precise positioning in complex settings. Additionally, the proposed method demonstrates a 45% improvement in computational efficiency compared to traditional multi-GNSS RTK/INS integration.