摘要
针对蚁群传统算法在计算GNSS整周模糊度过程中由于缺少初始信息素而产生搜索效率低下问题,提出改进的粒子群(PSO)与蚁群(ACO)混合搜索GNSS整周模糊度算法。在搜索初期,由于改进粒子群算法具备收敛速度快的优势,进行粗搜索获取次优解,并以此来初始化改进蚁群算法的信息素分布,最后完成整周模糊度的细搜索。基于经典算例和实测GPS/BDS数据进行了仿真对比实验,结果表明,改进的混合算法比单算法可以更快地收敛于最优解,搜索效率要明显优于LAMBDA算法,且解算的基线精度可以控制在3 mm以内(RMS),有效性和可靠性得到了较好的验证。
Aiming at the problem of low search efficiency caused by the lack of initial pheromone in the process of calculating GNSS ambiguity of GNSS in the traditional algorithm of ant colony,an improved particle swarm optimization(PSO)and ant colony hybrid GNSS algorithm is proposed.In the initial stage of the search,because the improved PSO algorithm has the advantage of fast convergence,a coarse search is carried out to obtain the sub-optimal solution,which is used to initialize the pheromone distribution of the improved ACO algorithm,and finally complete the fine search of the ambiguity of the whole week.Simulation comparison experiments based on classical examples and measured GPS/BDS data show that the improved hybrid algorithm can converge to the optimal solution faster than the single algorithm,the search efficiency is significantly better than the LAMBDA algorithm,and the baseline accuracy of the solution can be controlled within 3 mm(RMS).Effectiveness and reliability have been well verified.
作者
尚俊娜
王民顿
刘新华
王奕腾
SHANG Junna;WANG Mindun;LIU Xinhua;WANG Yiteng(School of communication Engineering,Hangzhou Dianzi University,Hangzhou 310018,China;Science and Technology on Communication Information Security Control Laboratory,Jiaxing 314033,China;No.36 Research Institute of CETC,Jiaxing 314033,China)
出处
《中国惯性技术学报》
EI
CSCD
北大核心
2021年第3期350-355,361,共7页
Journal of Chinese Inertial Technology
基金
国家自然科学基金青年基金项目(61701481)
中老北斗精密形变监测合作研究及示范(SBZ2019080054)
江苏省政策引导类计划(国际科技合作)--“一带一路”创新合作项目(BZ2019006)。
关键词
整周模糊度
改进粒子群算法
改进蚁群算法
混合算法
integer ambiguity
improved particle swarm optimization algorithm
improved ant colony optimization algorithm
hybrid algorithm
