Lower triangular Cholesky decomposition-based ambiguity correlation-lowering method

Abstract

The invention discloses a lower triangular Cholesky decomposition-based ambiguity correlation-lowering method and belongs to the satellite navigation and positioning technical field. According to themethod, LDL<T> decomposition is performed on an ambiguity variance-covariance matrix, so that a unit lower triangular matrix L and diagonal matrix D can be obtained; Gaussian elimination and conditional variances are adopted to exchange two integer transformation processes, so that the correlation of the off-diagonal elements of the lower triangular matrix L can be lowered, and therefore, elementsin the diagonal matrix D are sorted in an ascending order as much as possible; and finally, ambiguity search can be carried out to realize ellipsoid shape transformation, and the number of redundantinteger candidate nodes contained in a search space can be decreased, search time can be reduced, and ambiguity resolution efficiency can be improved.

Description

technical field [0001] The invention relates to the technical field of satellite navigation and positioning, in particular to an ambiguity reduction correlation method based on lower triangular Cholesky decomposition. Background technique [0002] The fast and accurate resolution of carrier phase integer ambiguity is a key issue in GNSS real-time high-precision dynamic positioning, and it has also been a hot issue in the field of GNSS research for many years. Only when the carrier phase ambiguity is accurately fixed can the carrier phase observations be converted into millimeter-level precision distance observations, thereby achieving high-precision navigation and positioning. Among many ambiguity resolution methods, the ambiguity resolution based on integer least squares as the estimation criterion has the highest success rate. In order to speed up the ambiguity search process, integer transformation is usually used to down-correlate the ambiguity variance-covariance matrix...

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Problems solved by technology

Lu Liguo et al. (2015) pointed out that the sorting direction of the conditional variance is the key to determine the efficiency of ambiguity resolution. When using different decomposition methods, the sorting direction must correspond to the decomposition method. Due to the difference in the decomposition method, the sorting direction of the conditional variance is completely different. Different conversion processes, so different decomposition methods will produce different ambiguity reduction related effects, resulting in certain differences in ambiguity resolution efficiency, especially for high-dimensional ambiguity resolution.

[0004] When decorrelation is performed on different ambiguity resolution data, the LAMBDA algorithm based on the upper triangular Cholesky decomposition and the multidimensional integer Gaussian transformation algorithm based on the lower triangular Cholesky decomposition will produce different solving effects with different data, which will cause The ambiguity search time is too long, which is not conducive to improving the stability of ambiguity resolution performance

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