GDOP-Based Low-Complexity LEO Satellite Subset Selection for Positioning

Low Earth orbit (LEO) satellites have recently received considerable attention because they can provide stronger signal power and better bandwidth availability than medium Earth orbit or geosynchronous orbit satellites. However, due to the limited processing capability of a receiver, it is difficult to utilize all the measurements of the available satellites in view when the number of satellites is large. With this motivation, selecting a subset of satellites that are in a good geometry relative to the receiver for precise positioning among a large number of available LEO constellations represents a challenging yet significant problem. Geometric dilution of precision (GDOP) is a metric that provides useful information about the relative geometry between satellites and a receiver. In this study, we put forth a novel GDOP-based satellite selection algorithm that uses efficient matrix decomposition and update rule. Simulation results show that the proposed algorithm achieves a GDOP performance close to the optimal exhaustive search-based schemes while greatly reducing the computational complexity. In particular, the computational complexity is verified in terms of flop counts as well as numerical evaluations.