Kalman filter is a commonly used method in the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation system, in which the process noise covariance matrix has a significant influence on the positioning accuracy and sometimes even causes the filter to diverge when using the process noise covariance matrix with large errors. Though many studies have been done on process noise covariance estimation, the ability of the existing methods to adapt to dynamic and complex environments is still weak. To obtain accurate and robust localization results under various complex and dynamic environments, we propose an adaptive Kalman filter navigation algorithm (which is simply called RL-AKF), which can adaptively estimate the process noise covariance matrix using a reinforcement learning approach. By taking the integrated navigation system as the environment, and the opposite of the current positioning error as the reward, the adaptive Kalman filter navigation algorithm uses the deep deterministic policy gradient to obtain the most optimal process noise covariance matrix estimation from the continuous action space. Extensive experimental results show that our proposed algorithm can accurately estimate the process noise covariance matrix, which is robust under different data collection times, different GNSS outage time periods, and using different integration navigation fusion schemes. The RL-AKF achieves an average positioning error of 0.6517 m within 10 s GNSS outage for GNSS/INS integrated navigation system and 14.9426 m and 15.3380 m within 300 s GNSS outage for the GNSS/INS/Odometer (ODO) and the GNSS/INS/Non-Holonomic Constraint (NHC) integrated navigation systems, respectively.

The combination of GNSS RTK and INS offers complementary advantages but faces significant challenges in urban canyons. Frequent cycle slips in carrier phase measurements and ambiguity resolution algorithms increase computational burden without improving positioning accuracy. Additionally, environmental interference introduces noise into observations, potentially leading to complete signal loss. To address these issues, this paper proposes a factor graph optimization (FGO) positioning algorithm incorporating predictive observation factors. First, a penalized spline (P-spline) is constructed to predict and smooth Doppler measurements. The predicted Doppler is then fused with the dynamics model predictions to enhance robustness. Using predictive Doppler, carrier phase and pseudorange observations are reconstructed, generating predictive constraint factors to improve positioning accuracy. Real-world tests conducted in urban canyons, including Shanghai, demonstrate that the proposed method maintains stable positioning performance even under short-term signal outages, effectively mitigating cumulative positioning errors caused by data loss. Compared to traditional methods that rely solely on available observations, the proposed algorithm improves northward and dynamic positioning accuracy by 35% and 29%, respectively, providing a highly robust navigation solution for complex urban environments.

GNSS (global navigation satellite systems) technology enables high-precision single-point positioning (SPP) in open environments. However, the accuracy of GNSS positioning is significantly compromised in complex urban canyons due to signal obstructions and non-line-of-sight propagation errors. To address this challenge, we propose a GNSS displacement estimation algorithm. This method learns nonlinear dependencies between GNSS raw measurements and corresponding position changes, capturing dynamic and layered features in GNSS measurement data for displacement estimation. We introduce a denoising auto-encoder (DAE) to preprocess raw GNSS observations, reducing the impact of noise. The model simultaneously outputs estimated displacement and model confidence. The fusion process dynamically combines positioning results from the SPP algorithm and the D-Tran model, adaptively blending them to achieve accurate and optimal positioning estimation. This approach optimizes the accuracy of estimated positioning results while maintaining confidence in the estimation. Experimental results show a 61% reduction in root mean square error (RMSE) and 100% availability in urban canyon environments compared to traditional single-point positioning techniques.

Accurately estimating the position and attitude of vehicles is essential for intelligent transportation systems. The GNSS/INS integrated system offers precise navigation information. However, the system’s positioning errors may accumulate rapidly in challenging GNSS signal conditions. Odometer (ODO) and nonholonomic constraints (NHC) are commonly employed to mitigate the rapid accumulation of INS errors. Compensating for the mounting angles of INS and the scale factor of the odometer is necessary to fully exploit the potential of ODO/NHC. However, many studies employ Kalman filters with small mounting angle assumption, which limits their applicability for large mounting angles in practice. To accurately estimate the mounting angle of INS with any installation attitude, we propose a new algorithm called Fast Estimation of Mounting Angle and Scale Factor (FEMASF). FEMASF employs Singular Value Decomposition (SVD) to obtain a closed-form solution for the parameters. It also incorporates an enhanced Sage-Husa scheme, enhancing overall estimation accuracy by reducing the weight of outlier data through a forgetting factor. Extensive simulation experimental results demonstrate that our proposed FEMASF algorithm outperforms filter-based methods in terms of accuracy and convergence speed for large mounting angles. Specifically, for the -90^∘ mounting angle, FEMASF achieves 0.45^∘ angle error, while the velocity-based Kalman filter (VKF) fails to converge and the position-based Kalman filter (PKF) yields about 4^∘ error. Furthermore, neither VKF nor PKF converges for the 120^∘ mounting angle, whereas FEMASF exhibits only about 3.2^∘ estimation error.