改进粒子群优化的卫星导航选星算法

为提高选星算法的性能，提出一种基于人工鱼群算法的粒子群优化（PSO）选星算法。该算法利用人工鱼群算法良好的全局收敛特性，克服了粒子群优化算法易陷入局部最优的缺点。将每种卫星组合看作空间中的一个粒子，选取几何精度因子（GDOP）作为适应度函数。利用所提算法更新粒子自身位置，优化卫星组合与几何精度因子。利用实际数据对所提算法进行验证和对比，结果表明:改进的选星算法在保障选星效率的同时，选星结果的准确性优于标准的粒子群优化选星算法。 

Satellite navigation satellite selection algorithm based on improved particle swarm optimization

In order to improve the performance of satellite selection algorithm, the Particle Swarm Optimization (PSO) satellite selection algorithm based on artificial fish swarm algorithm is proposed. Using the global convergence characteristics of artificial fish swarm algorithm, the algorithm can overcome the shortcomings of PSO algorithm that is easy to fall into local optimum. The improved algorithm treats each satellite combination as a particle in space, and the Geometric Dilution of Precision (GDOP) is chosen as the fitness function. The particle updates its position based on the optimization principle of the particle swarm optimization algorithm and artificial fish swarm algorithm, and the optimal satellite combination and GDOP value are obtained. The algorithms are verified and compared with real data, and the results show that the improved satellite selection algorithm not only guarantees the efficiency of the satellite selection, but also the accuracy of the satellite selection result is better than that of the satellite selection algorithm based on the PSO.