Joint optimization of observation weighting and GNSS integration parameters via GWO under blocking scenarios

Satellite visibility obstruction (blocking) is one of the primary limiting factors in Global Navigation Satellite System (GNSS) applications, as it reduces the number of observable satellites and degrades spatial geometry, thereby severely affecting positioning accuracy and reliability. This study proposes a novel framework for the simultaneous optimization of observation weighting and multi-constellation GNSS integration under blocking conditions. Within this framework, the determination of weighting coefficients in the Weighted Least Squares (WLS) solution is formulated as a multi-objective optimization problem, and adaptive, data-driven observation weights are derived using the Grey Wolf Optimizer (GWO) evolutionary algorithm. The objective functions simultaneously account for minimizing positioning error, quantified by the Root Mean Square (RMS), and improving satellite geometry based on the Dilution of Precision (DOP) index. The proposed method is evaluated using real GNSS data in both static and dynamic scenarios under severe sky-view obstruction conditions. The results demonstrate that the proposed approach, when applied to Integrated multi-constellation GNSS (IGNSS), achieves an average improvement in DOP of approximately 66%, 52%, and 77%, along with an average reduction in RMS positioning error of approximately 52%, 58%, and 49%, respectively, across the three investigated scenarios. Furthermore, comparative analysis with other optimization algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Simulated Annealing (SA) indicates faster convergence and higher solution stability for the GWO-based approach. Overall, the findings confirm that the proposed framework provides a robust and effective solution for enhancing the accuracy and reliability of multi-constellation GNSS positioning in challenging, blocking-dominated environments.