引用本文: 郭迎庆, 詹洋, 张琰, 王译那, 徐赵东, 李今保. 基于PSO与AFSA的GNSS整周模糊度种群融合优化算法[J]. 工程科学学报.
Population Fusion Optimization Algorithm for GNSS Integer Ambiguity Resolution Based on PSO and AFAS[J]. Chinese Journal of Engineering.
 Citation: Population Fusion Optimization Algorithm for GNSS Integer Ambiguity Resolution Based on PSO and AFAS[J]. Chinese Journal of Engineering.

## Population Fusion Optimization Algorithm for GNSS Integer Ambiguity Resolution Based on PSO and AFAS

• 摘要: 载波相位测量是实现全球导航卫星系统（GNSS）快速高精度定位的重要途径，而准确解算整周模糊度是其中的关键步骤之一。粒子群算法(PSO)收敛速度快但易陷入局部最优，人工鱼群算法(AFSA)全局优化性能好但收敛速度慢，因此融合两种优化算法的优点，提出一种GNSS整周模糊度种群融合优化算法（PSOAF）。首先，通过载波相位双差方程求解整周模糊度的浮点解和对应的协方差矩阵。然后，采用反整数Cholesky算法对模糊度浮点解作降相关处理。其次，针对整数最小二乘估计的不足通过优化适应度函数来提高算法的收敛性和搜索性能。最后，通过PSOAF算法对整周模糊度进行解算。通过经典算例和试验研究表明：PSOAF算法可以更快地收敛于最优解，搜索效率也更为出色，解算的基线精度可以控制在10 mm以内，在短基线的实际情况下具有较高的应用价值。

Abstract: Carrier phase measurement is an important way to realize fast and high-precision positioning of Global Navigation Satellite System (GNSS), and accurate solving of whole-week ambiguity is one of the key steps. Particle swarm algorithm (PSO) has fast convergence speed but is easy to fall into local optimum, and artificial fish swarm algorithm (AFSA) has good global optimization performance but slow convergence speed, so a GNSS perimeter ambiguity population fusion optimization algorithm (PSOAF) is proposed by combining the advantages of the two optimization algorithms. First, the floating-point solution of the whole-week ambiguity and the corresponding covariance matrix are solved by the carrier phase double difference equation. Then, the inverse integer Cholesky algorithm is used to down-correlate the floating-point solution of the fuzziness. Next, the convergence and search performance of the algorithm are improved by optimizing the fitness function for the deficiency of integer least squares estimation. Finally, the whole week fuzzy degree is solved by PSOAF algorithm. Classical examples and experimental studies consistently illustrate the PSOAF algorithm's ability to swiftly

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