The safe operation of unmanned ground vehicles (UGVs) demands fundamental and essential requirements for continuous and reliable positioning performance. Traditional coupled navigation systems, combining the global navigation satellite system (GNSS) with an inertial navigation system (INS), provide continuous, drift-free position estimation. However, challenges like GNSS signal interference and blockage in complex scenarios can significantly degrade system performance. Moreover, ultra-wideband (UWB) technology, known for its high precision, is increasingly used as a complementary system to the GNSS. To tackle these challenges, this paper proposes a novel tightly coupled INS/UWB/GNSS-RTK integrated positioning system framework, leveraging a variational Bayesian adaptive Kalman filter based on the maximum mixture correntropy criterion. This framework is introduced to provide a high-precision and robust navigation solution. By incorporating the maximum mixture correntropy criterion, the system effectively mitigates interference from anomalous measurements. Simultaneously, variational Bayesian estimation is employed to adaptively adjust noise statistical characteristics, thereby enhancing the robustness and accuracy of the integrated system’s state estimation. Furthermore, sensor measurements are tightly integrated with the inertial measurement unit (IMU), facilitating precise positioning even in the presence of interference from multiple signal sources. A series of real-world and simulation experiments were carried out on a UGV to assess the proposed approach’s performance. Experimental results demonstrate that the approach provides superior accuracy and stability in integrated system state estimation, significantly mitigating position drift error caused by uncertainty-induced disturbances. In the presence of non-Gaussian noise disturbances introduced by anomalous measurements, the proposed approach effectively implements error control, demonstrating substantial advantages in positioning accuracy and robustness.

For the current problem of cycle slips in the observation data of the BDS-2 and BDS-3 (Bei Dou Navigation Satellite System), in this paper, BDS B1I and B3I signals are used as research objects to study the detection of cycle slips, and their repair algorithm. The Geometry-free (GF) and Melbourne–Wübeena (MW) combination algorithm are used for the detection of cycle slips. A new method of arc partition is proposed in this work to detect cycle slips as the boundary to delimit two different observation arcs. In this way, the different values of cycle slips can be divided and marked. Moreover, the gross errors can be removed. Finally, the detection of cycle slips and the analysis of all epochs can be completed and repaired. This work also analyzes the dual-frequency data effect of cycle slips on code multipath observation. The results showed that this method greatly improved the speed of detection of cycle slips.