In this article, we propose a new particle swarm optimization–based satellite selection algorithm for BeiDou Navigation Satellite System/Global Positioning System receiver, which aims to reduce the computational complexity of receivers under the multi-constellation Global Navigation Satellite System. The influences of the key parameters of the algorithm—such as the inertia weighting factor, acceleration coefficient, and population size—on the performance of the particle swarm optimization satellite selection algorithm are discussed herein. In addition, the algorithm is improved using the adaptive simulated annealing particle swarm optimization (ASAPSO) approach to prevent converging to a local minimum. The new approach takes advantage of the adaptive adjustment of the evolutionary parameters and particle velocity; thus, it improves the ability of the approach to escape local extrema. The theoretical derivations are discussed. The experiments are validated using 3-h real Global Navigation Satellite System observation data. The results show that in terms of the accuracy of the geometric dilution of precision error of the algorithm, the ASAPSO satellite selection algorithm is about 86% smaller than the greedy satellite selection algorithm, and about 80% is less than the geometric dilution of precision error of the particle swarm optimization satellite selection algorithm. In addition, the speed of selecting the minimum geometric dilution of precision value of satellites based on the ASAPSO algorithm is better than that of the traditional traversal algorithm and particle swarm optimization algorithm. Therefore, the proposed ASAPSO algorithm reduces the satellite selection time and improves the geometric dilution of precision using the selected satellite algorithm.

在本文中，我们提出了一种新的基于粒子群优化的北斗导航卫星系统/全球定位系统接收机选星算法，旨在降低多星座全球导航卫星系统下接收机的计算复杂度。本文讨论了算法的关键参数——如惯性权重因子、加速度系数和种群大小——对粒子群优化选星算法性能的影响。此外，该算法使用自适应模拟退火粒子群优化 (ASAPSO) 方法进行改进，以防止收敛到局部最小值。新方法利用了进化参数和粒子速度的自适应调整；因此，它提高了方法逃避局部极值的能力。讨论了理论推导。这些实验使用 3 小时真实的全球导航卫星系统观测数据进行了验证。结果表明，在算法精度误差几何稀释的精度方面，ASAPSO选星算法比贪心选星算法小86%左右，比精度误差几何稀释小80%左右粒子群优化卫星选择算法。此外，基于ASAPSO算法选择卫星精度值最小几何稀释度的速度优于传统的遍历算法和粒子群优化算法。所以，