Research on BDS/GPS Integrated Navigation Satellite Selection Algorithm Based on Particle Swarm Optimization

In the multi-constellation satellite navigation system, all the visible satellites are used for positioning, which will increase the computation amount of the receiver and affect the real-time positioning. How to quickly and effectively select visible satellites for positioning is a research topic. For this problem, a satellite selection algorithm based on Particle Swarm Optimization (PSO) is proposed. In this method, the visible satellite is numbered, random grouping, and each group as a particle; the velocity-displacement model in the PSO keeps the particles gradually close to the minimum value of the GDOP. Under a series of simulation experiments, the key parameters such as inertia weight factor, acceleration coefficient and maximum velocity of PSO are determined. Besides, local search based on chaos mechanism are introduced, which can avoid the results of PSO algorithm into local optimum. Finally, the performance of satellite selection with PSO is confirmed to be remarkable by the simulation experiments under different numbers of selected satellites. The results show that this method can quickly select satellites under BDS and GPS system, and the result meets receiver positioning accuracy.