A fast satellite selection method for GNSS systems based on multi-strategy grey wolf optimization algorithm

The rapid advancement of GNSS now enables GNSS terminals to simultaneously observe dozens of satellites. Nevertheless, implementing positioning solutions for all observed satellites would substantially escalate the demand for computing resources and performance capabilities of small mobile terminals. To address this issue, this paper introduces an efficient satellite selection algorithm utilizing the multi-strategy grey wolf optimization (MSGWO) algorithm. The MSGWO method integrates the grey wolf optimizer (GWO) algorithm while incorporating the crossover strategy from variable neighborhood search (VNS) and the mutation mechanism from genetic algorithm (GA). Therefore, it can significantly mitigate the issues of premature convergence to local optima and inadequate population diversity in the GWO algorithm. The MSGWO algorithm is validated and comparatively analyzed using empirical data. The research findings indicate that, within a multi-system environment, the computational efficiency of the MSGWO algorithm improves by 92.38% compared to the exhaustive method. This algorithm is especially well-suited for addressing the complex problem of satellite selection in multi-constellation systems.