Parallel LLL high-dimensional ambiguity decorrelation algorithm

An ambiguity and drop-correlation technology, which is applied in the field of satellite navigation and positioning, can solve the problems of convergence deterioration and cannot efficiently deal with high-dimensional ambiguity drop-correlation solutions, so as to overcome convergence stability, improve efficiency, improve computing efficiency and search speed effect

Inactive Publication Date: 2015-10-28
WUHAN UNIV
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[0008] The present invention mainly provides a parallel LLL high-dimensional ambiguity reduction correlation method, which can effectively overcome the accumulation of rounding errors in the orthogonal iter

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  • Parallel LLL high-dimensional ambiguity decorrelation algorithm

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[0033] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0034] The object of the present invention is to provide a kind of parallel LLL high-dimensional ambiguity reduction correlation method, which adopts mixed Cholesky lower triangle (L T L) decomposition and upper triangular (U T U) decomposition, which can effectively decorrelate high-dimensional ambiguity and improve the search efficiency and calculation speed of ambiguity. At the same time, before each decomposition, the vectors of the correlation matrix are sorted according to the size of the inner product, thereby improving the convergence of the algorith...

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Abstract

The invention discloses a parallel LLL high-dimensional ambiguity decorrelation algorithm. The algorithm includes: firstly, the calculating efficiency of the LLL algorithm aiming to high-dimensional ambiguity decorrelation is improved and the ability of high-dimensional ambiguity decorrelation is enhanced by mixed adoption of Cholesky lower triangular LTL decomposition and an upper triangular UTU decomposition; secondly, in order to obtain a Z transformation matrix with high decorrelation ability, in each QR decomposition transformation process, a transformation coefficient matrix needs to obtain a smaller integer value so that before each time of lower triangular decomposition, row vectors of an ambiguity covariance matrix are in an ascending order according to the inner products, and column vectors of the matrix are arranged in a descending order according to the inner products before the upper triangular decomposition, in this way, the Z transformation decorrelation performance is better; and finally, the rounding operation of the algorithm in an orthogonal transformation process is shifted to the operation process of the Z matrix, error accumulation caused by repeated rounding operation in the algorithm iteration process can be avoided, the problem of algorithm divergence is solved, and the calculating efficiency and stability of the parallel LLL algorithm can be further improved.

Description

technical field [0001] The invention belongs to the technical field of satellite navigation and positioning, and relates to a parallel LLL (A.K. Lenstra, H.W. Lenstra, L. Lovasz, LLL) high-dimensional ambiguity reduction correlation algorithm. Background technique [0002] The ambiguity reduction correlation algorithm is a key issue in the data processing stage of GNSS satellite navigation and positioning. Fast and accurate resolution of the integer ambiguity values ​​in phase observations can improve the real-time positioning and positioning accuracy, and ambiguity reduction correlation is the premise of fast and efficient resolution of high-dimensional integer ambiguities. In the integer ambiguity resolution based on the ambiguity domain, due to the low accuracy of the real ambiguity parameters obtained by least squares and the strong correlation between them, this will lead to a serious loss of search efficiency. decline. Therefore, before searching the ambiguities, i...

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Application Information

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IPC IPC(8): G01S19/37G01S19/44
CPCG01S19/37G01S19/44
Inventor 郑建生苏明坤杨艳茜陈鲤文
Owner WUHAN UNIV
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