一种矩阵分块的GNSS相位模糊度快速解算方法

为解决GNSS模糊度解算中最小二乘模糊度去相关（least-square ambiguity decorrelation adjustment，LAMBDA）算法涉及大量矩阵运算、降相关耗时较长等问题，提出一种对条件方差矩阵进行分块的最小二乘模糊度去相关（blocking in least-square ambiguity decorrelation adjustment，BLAMBDA）算法。该算法对条件方差矩阵进行分块，减少条件方差排序次数，并在此基础上对Cholesky分解公式进行整合，减少Cholesky分解过程中的数乘运算。仿真实验和实测结果表明，与LAMBDA算法相比，BLAMBDA算法的整体解算效率提升显著，且更加稳定。

A Fast Solution Method for GNSS Phase Ambiguity of Matrix Partitioning

Least-square ambiguity decorrelation adjustment(LAMBDA) algorithm involves a lot of matrix operations and takes a long time to reduce correlation in GNSS ambiguity resolution. So, we propose a blocking in least-square ambiguity decorrelation adjustment(BLAMBDA) algorithm for conditional variance matrix partitioning. In this algorithm, the conditional variance matrix is divided into blocks to reduce the ranking times of conditional variance, and on this basis, the Cholesky decomposition formula is integrated to reduce the number multiplication operation in the process of Cholesky decomposition. Simulation experiments and measured results show that, compared with LAMBDA algorithm, the overall efficiency of BLAMBDA algorithm improves significantly, and the BLAMBDA algorithm is more stable.