A Fast Satellite Selection Algorithm Based on Hierarchical Clustering and Iterative Subset Optimization

When calculating positions, finding the optimal satellite subset is often a compromise between real-time performance and accuracy. This paper proposes a satellite selection method based on hierarchical clustering and iterative optimization. First, hierarchical clustering groups satellites on a two-dimensional projection plane are used to obtain a basic satellite subset. The relationship between GDOP and the number of satellites involved in positioning calculations is analyzed, showing that once the number of satellites reaches a certain threshold, further increases do not significantly enhance positioning accuracy. Then, the impact of a given satellite on the GDOP of the current satellite subsets is analyzed. Based on this, the most important satellite is iteratively added to the current satellite subset, gradually optimizing the spatial geometric configuration of the satellite subset. Simulations show that this method can quickly select an optimal satellite subset that meets positioning accuracy requirements under different GDOP demands, significantly improving computational efficiency compared to the traditional methods.