基于PSO与AFSA的GNSS整周模糊度种群融合优化算法  

Population fusion optimization algorithm for GNSS integer ambiguity resolution based on PSO and AFSA

作　　者：郭迎庆[1] 詹洋 张琰 王译那 徐赵东[2] 李今保[3] GUO Yingqing;ZHAN Yang;ZHANG Yan;WANG Yina;XU Zhaodong;LI Jinbao(College of Mechanical and Electronic Engineering,Nanjing Forestry University,Nanjing 210037,China;China–Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures,Southeast University,Nanjing 211189,China;Jiangsu Southeast Special Engineering&Technology Co.,Ltd.,Nanjing 210008,China)

机构地区：[1]南京林业大学机械电子工程学院,南京210037 [2]东南大学中国–巴基斯坦重大基础设施智慧防灾“一带一路”联合实验室,南京211189 [3]江苏东南特种技术工程有限公司,南京210008

出　　处：《工程科学学报》2024年第12期2246-2256,共11页Chinese Journal of Engineering

基　　金：江苏省前沿引领技术基础研究重大项目(BK20222006)。

摘　　要：载波相位测量是实现全球导航卫星系统(Global navigation satellite system, 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(Particle swarm optimization, PSO)收敛速度快但易陷入局部最优,人工鱼群算法(Artificial fish swarm algorithm, AFSA)全局优化性能好但收敛速度慢,因此融合两种算法的优点,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.Carrier phase measurement plays a crucial role in achieving rapid and high-precision positioning within a global navigation satellite system(Global navigation satellite system,GNSS).A pivotal aspect of this process is the accurate resolution of the integer ambiguity.Although the particle swarm optimization algorithm(Particle swarm optimization,PSO)demonstrates quick convergence,it tends to become trapped in local optima,showing a relatively weak ability to fix ambiguity.Conversely,the artificial fish school algorithm(Artificial fish swarm algorithm,AFSA)excels the global optimization performance.However,its natural selection mode,which operates without a“leader,”renders the integer ambiguity resolution process more time-consuming.By integrating the strengths of PSA and AFSA,we propose an improved hybrid algorithm,termed the particle swarm and artificial fish swarm(PSOAF)algorithms,to efficiently search for integer ambiguity solutions in GNSS.The process begins by solving the floating-point solution and its corresponding covariance matrix using the carrier phase double-difference equation.Then,to address the correlation issue,the inverse integer Cholesky algorithm is used to effectively decorrelate them.Recognizing the limitations inherent in integer least squares estimation,we further refine the effectiveness of the PSOAF algorithm by optimizing the fitness function.This optimization significantly enhances the convergence speed and search performance of the algorithm,resulting in a precise resolution of the integer ambiguity.In the initial stage of integer ambiguity search,the PSO’s characteristic of rapid convergence facilitates a coarse search,yielding a suboptimal solution.This solution serves as foundational data for the AFSA,guiding the fine search required for integer ambiguity resolution.To verify the PSOAF algorithm’s effectiveness and practicality,we conducted both three-dimensional and twelve-dimensional simulation analyses based on a classical example.The results demonstrate that the PSOAF algo

关 键 词：全球导航卫星系统(GNSS) 整周模糊度 粒子群算法 人工鱼群算法 融合算法 

分 类 号：P288.4[天文地球—地图制图学与地理信息工程]