一种快速解算高维模糊度的改进模拟植物生长算法  

Modified plant growth simulation algorithm for fast high-dimensional integer ambiguity resolution

作　　者：孙妍艳 杨恒赞 刘翠芝 常乐[3] 魏二虎[4] SUN Yanyan;YANG Hengzan;LIU Cuizhi;CHANG Le;WEI Erhu(Department of Surveying and Mapping Engineering,School of Resources and Civil Engineering,Northeastern University,Shenyang 110819,China;Department of Automation,College of Information Science and Engineering,Northeastern University,Shenyang 110819,China;Department of Civil Engineering,Shenyang Urban Construction University,Shenyang 110167,China;School of Geodesy and Geomatics,Wuhan University,Wuhan 430079,China)

机构地区：[1]东北大学资源与土木工程学院测绘工程系,沈阳110819 [2]东北大学信息科学与工程学院自动化系,沈阳110819 [3]沈阳城市建设学院土木工程系,沈阳110167 [4]武汉大学测绘学院,武汉430079

出　　处：《测绘科学》2020年第2期35-42,共8页Science of Surveying and Mapping

基　　金：国家重点研发计划项目(2018YFC1503600);国家自然科学基金项目(41874036).

摘　　要：针对整数最小二乘模糊度降相关平差(LAMBDA)算法解算高维整周模糊度效率比较低的问题,该文从模糊度解算一般规则是一个非线性整数规划问题的角度出发,提出了一种改进模拟植物生长算法。该算法是一种智能优化算法。通过多组高维模拟数据和实测数据,将该文算法与LAMBDA算法及MLAMBDA算法进行了对比分析。结果显示,当模糊度维数等于45维和50维时,该文算法在运算效率上略优于LAMBDA算法。当维数达到55维及以上时,相比于LAMBDA和MLAMBDA算法运算速度分别提高了至少52.8%和19.2%。因此改进模拟植物生长算法对于快速固定高维整周模糊度具有一定的应用参考价值。Solving the problem of the less efficient in solving high dimensional ambiguities for the internationally recognized ambiguity resolution method that is the least-squares ambiguity decorrelation adjustment(LAMBDA).This paper proposed a modified plant growth simulated algorithm from the perspective of the general rule of ambiguity solving that is a nonlinear integer programming problem.This algorithm is an intelligent optimization algorithm.The simulation data and measured data were used to compare the algorithm with LAMBDA and MLAMBDA.The results showed that when the dimension was equal to 45 or 50,the algorithm was slightly better than LAMBDA in computational efficiency.When the dimension reached 55 and above,the running speed was increased by at least 52.8%and 19.2%,respectively,compared to the LAMBDA and MLAMBDA.Therefore,the modified plant growth simulation algorithm has certain application reference value for fast fixing high-dimensional ambiguity.

关 键 词：高维模糊度 LAMBDA算法 MLAMBDA算法 改进模拟植物生长算法 智能优化算法 

分 类 号：P228.4[天文地球—大地测量学与测量工程]