3种GNSS模糊度降相关算法的时间效率对比分析

Time Efficiency Comparison and Analysis of Three GNSS Ambiguity Decorrelation Algorithms

常用的降相关算法评价方法(包括条件数、降相关系数和候选值个数)都不能对降相关算法作全面评估,因此,基于算法的时间效率(包括降相关时间效率和搜索时间效率)对降相关算法作综合对比分析。利用仿真和实测数据对3种主流的降相关算法进行时间效率评价,实验表明,整数Gauss算法效率最好,双Cholesky算法次之,LLL-IGS效果最差。

The decorrelation algorithm can reduce the correlation of the variance-covariance of the ambiguity,decrease the candidate numbers effectively,and further increase the effect of the search.The assessment measures contain condition numbers,decorrelation coefficients and candidate numbers.However,the condition number and decorrelation coefficient can only assess the performance of decorrelation and the candidate number merely assesses search efficiency.None of these can assess the decorrelation algorithm comprehensively.Hence,in this contribution,we adopt time efficiency to assess the decorrelation algorithms.Time efficiency includes decorrelation time efficiency and search time efficiency.Simulation and real GNSS data are used to compare and analyze the three decorrelation algorithms by time efficiency measure.In terms of time efficiency,the results of the experiment show that the integer Gauss algorithm performs best,the pair Cholesky performs slightly worse than Gauss,and LLL-IGS performs worst.