基于梯度优化器融合策略的快速选星方法OA

To meet the requirements of"high precision,low time consumption and strong robustness"of satellite selection algorithms for satellite positioning in complex environments,a fast satellite selection method based on the fusion strategy of gradient-based optimizer(GBO)is proposed.This method integrates the GBO algorithm with the Levy flight mechanism combined with Cauchy mutation and Rice mutation that is multi-mutation gradient-based optimizer(MMGBO)method,forming a multi-mode mutation mechanism.It effectively enhances the global exploration ability and local escape ability of the GBO algorithm,while alleviating the stagnation problem in the later stage of convergence.Simulation experiments show that the positioning accuracy of the MMGBO algorithm is superior to that of the single GBO algorithm,and the GDOP error compared with the traversal method is generally close to zero.The calculation efficiency improvement ratio can even reach more than 99%.The proposed method provides an efficient and reliable satellite selection scheme for real-time high-precision positioning of GNSS combined with low-Earth orbit satellites,and can be adapted to multi-system fusion positioning scenarios such as BeiDou Navigation Satellite System(BDS)and GPS.