Householder正交变换在模糊度降相关算法中的应用  

Decorrelation Algorithm Based on Householder Orthogonal Transformation

作　　者：谢恺[1] 柴洪洲[1] 范龙[1,2] 明锋[1] 

机构地区：[1]信息工程大学,河南郑州450052 [2]海军海洋测绘研究所,天津300061

出　　处：《测绘科学技术学报》2014年第1期28-33,共6页Journal of Geomatics Science and Technology

基　　金：国家自然科学基金项目(41274045)

摘　　要：在GNSS模糊度解算的过程中,由于模糊度之间存在相关性,为减少搜索时间需要对模糊度的协方差矩阵进行降相关处理。降相关算法的优劣将直接影响到模糊度搜索的效率。本文基于Householder正交变换提出了一种新的降相关算法,并利用随机模拟数据和北斗实测数据,从谱条件数、平均相关系数和规约时间3个方面将Householder算法与目前较为流行的LLL算法以及逆整数Cholesky算法进行了对比。通过实验分析得出,Householder算法能够明显改善降相关处理的效果。但是该算法仍存在规约时间较长的不足,需要进一步完善。In the process of GNSS ambiguity resolution,since the ambiguities are correlated,in order to reduce the searching time,the ambiguity covariance matrix needs to be decorrelated. The quality of the decorrelation algorithm will have an impact on the ambiguity searching efficiency directly. In this paper,a new decorrelation algorithm was proposed based on Householder orthogonal transformation. The random simulated data and Compass data were used to compare Householder algorithm with the popular LLL algorithm and inverse integer Cholesky algorithm from aspects of spectral condition number, average correlation coefficient and reduction time. The results showed that Householder algorithm could improve the decorrelation effect obviously. But the algorithm had a disadvantage of relatively long reduction time which needed further improvement.

关 键 词：降相关 正交变换 Householder算法 LLL算法 逆整数Cholesky算法 

分 类 号：P228[天文地球—大地测量学与测量工程]