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CN-122017903-A - GNSS part ambiguity fixed acceleration method based on isomorphism multiplexing

CN122017903ACN 122017903 ACN122017903 ACN 122017903ACN-122017903-A

Abstract

The invention provides a GNSS partial ambiguity fixed acceleration method based on isomorphism multiplexing, which comprises the steps of systematically recording the decorrelation transformation information of a covariance matrix of a first ambiguity subset after the first complete decorrelation calculation is carried out, multiplexing the recorded transformation information by utilizing the structural isomorphism between the covariance matrix of a current subset and the covariance matrix of a previous subset when a subsequent ambiguity subset is processed, pre-transforming the covariance matrix of the current subset to a state which is closer to the decorrelation characteristic, and taking the covariance matrix as an efficient starting point of iterative optimization, thereby avoiding completely independent and time-consuming decorrelation operation on the covariance matrix of each subset from zero. The invention obviously reduces the related integral calculation burden of sequential reduction in the partial ambiguity fixing process, greatly improves the calculation efficiency, and is particularly suitable for low-cost GNSS receivers with strict real-time requirements or limited calculation force and other high-real-time application platforms.

Inventors

  • ZHANG XIAOHONG
  • Ken Ga
  • Liu wanke
  • LIU YING

Assignees

  • 武汉大学

Dates

Publication Date
20260512
Application Date
20260228

Claims (10)

  1. 1. The GNSS part ambiguity fixed acceleration method based on isomorphism multiplexing is characterized by comprising the following steps of: Establishing an observation equation through the GNSS observation value, and calculating to obtain a floating point estimation value of the corresponding carrier phase ambiguity and a corresponding original covariance matrix by utilizing a least square principle; Performing first time down correlation calculation on the original covariance matrix to obtain a parent set down correlation transformation matrix, and enabling a matrix obtained by multiplying the parent set down correlation transformation matrix, the original covariance matrix and a transpose matrix of the parent set down correlation transformation matrix to be close to a diagonal matrix; Removing any ambiguity from a current ambiguity set according to a partial ambiguity fixed PAR strategy to form an ambiguity subset, and executing sequential decreasing correlation acceleration operation on the ambiguity subset to obtain a new decreasing correlation transformation matrix of the ambiguity subset; and taking the ambiguity subset as a new father set, repeatedly executing sequential decreasing correlation acceleration operation based on isomorphism by adopting a PAR strategy based on the new decreasing correlation transformation matrix until a PAR preset stopping condition is met, and generating an ambiguity subset with a plurality of dimensionalities decreasing in sequence.
  2. 2. The isomorphic multiplexing-based GNSS partial ambiguity fixed acceleration method of claim 1, wherein removing any one ambiguity from a current ambiguity set according to a partial ambiguity fixed PAR strategy to form an ambiguity subset, performing a sequential decorrelation acceleration operation on the ambiguity subset to obtain a new decorrelation transformation matrix of the ambiguity subset, comprising: based on the parent set, reducing the correlation transformation matrix Generating intermediate transformation matrices by elementary rank transformation , Subset covariance matrix corresponding to subset with ith ambiguity removed Intermediate matrix transformed into near diagonal matrix ; In the intermediate matrix Performing a decorrelation calculation as an iteration start to obtain a covariance matrix for the subset Is a new decorrelation transformation matrix 。
  3. 3. The isomorphic multiplexing-based GNSS partial ambiguity fixed acceleration method of claim 2, wherein the set of alienation transformation matrices is based on Generating intermediate transformation matrices by elementary rank transformation Comprising: Down-correlating the parent set to a transformation matrix Multiplying by rank permutation operation Is inverse to the matrix to obtain a matrix Wherein , Representing a transformation matrix that line-swaps i with n rows; Pair matrix To perform Gaussian integer elimination, and set the single-mode transformation matrix as So as to satisfy Through the process of The last column of the transformation realization is 0, and the last element of the last column is 1; Taking the transformation matrix Front of (2) The block matrix is 。
  4. 4. The isomorphic multiplexing-based GNSS partial ambiguity fixed acceleration method according to claim 2, wherein the intermediate matrix is used As an iteration start, it includes: Calculating the intermediate matrix Intermediate matrix to be calculated Sending the fuzzy degree to an LAMBDA method for fixation of the fuzzy degree; Wherein the intermediate matrix is calculated Compared to the non-transformed subset covariance matrix And the method has weaker off-diagonal correlation, and reduces the integer Gaussian transformation and the number of conditional variance reordering times required by subsequent decorrelation iteration.
  5. 5. The isomorphic multiplexing-based GNSS partial ambiguity fixed acceleration method of claim 3, wherein the single mode transformation matrix is The absolute value of the determinant is 1, and the elements are integers, and the matrix is obtained The last column is subjected to integer Gaussian elimination construction, so that the construction is finished The matrix remains an integer single-mode matrix.
  6. 6. The isomorphic multiplexing-based GNSS partial ambiguity fixed acceleration method according to claim 1, wherein the repeated execution of the isomorphic-based sequential downcorrelation acceleration operation using PAR strategy based on the new downcorrelation transformation matrix with the ambiguity subset as a new parent set, comprises: Processing sequentially a plurality of subsets of ambiguities formed by successively culling ambiguities; for the (i+1) th subset, its sequential downcorrelation acceleration operation multiplexes the downcorrelation transform matrix information of the (i) th subset or parent set.
  7. 7. A isomorphic multiplexing-based GNSS partial ambiguity fixed acceleration system, comprising: The establishing module is used for establishing an observation equation through the GNSS observation value, and calculating to obtain a floating point estimation value of the corresponding carrier phase ambiguity and a corresponding original covariance matrix by utilizing a least square principle; The calculation module is used for performing first time down correlation calculation on the original covariance matrix to obtain a parent set down correlation transformation matrix, and enabling a matrix obtained by multiplying the parent set down correlation transformation matrix, the original covariance matrix and a transpose matrix of the parent set down correlation transformation matrix to be close to a diagonal matrix; The acceleration module is used for removing any ambiguity from the current ambiguity set according to the PAR strategy to form an ambiguity subset, and performing sequential decorrelation acceleration operation on the ambiguity subset to obtain a new decorrelation transformation matrix of the ambiguity subset; And the iteration module is used for taking the ambiguity subset as a new father set, repeatedly executing sequential decreasing correlation acceleration operation based on isomorphism by adopting a PAR strategy based on the new decreasing correlation transformation matrix until a PAR preset stop condition is met, and generating an ambiguity subset with a plurality of dimensionalities decreasing in sequence.
  8. 8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the isomorphic multiplexing based GNSS partial ambiguity fix acceleration method according to any of claims 1 to 6 when executing the program.
  9. 9. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the isomorphic multiplexing based GNSS partial ambiguity fixed acceleration method according to any of claims 1 to 6.
  10. 10. A computer program product comprising a computer program which, when executed by a processor, implements a GNSS partial ambiguity fixed acceleration method based on isomorphic multiplexing according to any of claims 1 to 6.

Description

GNSS part ambiguity fixed acceleration method based on isomorphism multiplexing Technical Field The invention relates to the technical field of navigation positioning, in particular to a GNSS part ambiguity fixed acceleration method based on isomorphism multiplexing. Background In the relative positioning of the carrier phases of the high-precision GNSS, the correct fixation of the whole-cycle ambiguity is a key for realizing the positioning precision of the centimeter level or even the millimeter level. The mathematical model is usually expressed as a mixed integer least squares problem. Currently, the most dominant ambiguity fixing method is the LAMBDA (Least-squares Ambiguity Decorrelation Adjustment) method and its variants proposed by Teunissen. Furthermore, the core flow of the method comprises two main steps, firstly "decorrelating" (decorrelation) or "lattice reduction" (lattice basis reduction) the variance-covariance matrix of ambiguity by integer gaussian transformation (Integer Gaussian Transformations, IGTs) and conditional variance reordering (Permutation) to weaken the correlation between ambiguity parameters, thus compressing the search space, and then performing an efficient integer search (e.g. SE-VB search) within the transformed space to determine the optimal integer ambiguity vector. With the development of multi-frequency multi-system GNSS, the number of available satellites is significantly increased, while the observation redundancy is improved, the ambiguity dimension is increased, and in a complex observation environment (such as multipath effect and ionospheric disturbance), the success rate and reliability of fixing all ambiguities at the same time may be reduced. For this reason, a partial ambiguity fix (Partial Ambiguity Resolution, PAR) strategy has developed. PAR gives up ambiguities that are difficult to fix by screening and fixing a reliable subset of ambiguities to improve overall fixing success rate and positioning robustness. In the original ambiguity domain, the "unreliable" ambiguity (e.g., based on the criteria such as ratio test value increase, ADOP change, or residual analysis) is removed by iteration, and the fixed procedure is repeatedly executed, so that the method is a mainstream PAR strategy which has definite physical meaning and is widely adopted. However, this iterative PAR strategy introduces a significant computational burden while promoting robustness. In each iteration, after one ambiguity is removed, the complete LAMBDA decorrelation and search process is re-executed on the variance-covariance matrix with reduced dimensions corresponding to the remaining ambiguity subset. Studies have shown that the computation of the downcorrelation (lattice reduction) is much more time consuming than the integer search in the processing dimensions common to GNSS (10-40 dimensions). Prior studies, such as the MLAMBDA algorithm proposed by Chang et al, and many subsequent improvements, focused mainly on how to accelerate the de-correlation process for a single given Gao Weixie variance matrix. Although these approaches achieve significant success, they have a fundamental efficiency bottleneck in applying to the iterative PAR scenario described above, in continuously culling out ambiguities and generating a series of nested ambiguity subsets, there is a high degree of structural similarity (isomorphism) between the covariance matrices corresponding to the front and back subsets. The current decorrelation algorithm treats the covariance matrix of the current subset as a brand new independent matrix at each iteration, and completely ignores the inherent relation between the covariance matrix and the (parent set) matrix processed in the previous iteration, so that a great number of repeated and unnecessary calculations are performed. This repeated computation severely constrains the application of PAR methods to high real-time demanding platforms (e.g., autopilot, drone) or computationally limited low cost GNSS receivers. Therefore, how to fully utilize the structural similarity between covariance matrixes of adjacent subsets in the PAR flow and design a sequential descent correlation method capable of multiplexing historical descent correlation information, so as to avoid independent and complete descent correlation calculation of each subset matrix from zero, and the method becomes a key problem for improving the overall efficiency of iterative PAR and promoting wider application of the iterative PAR. Disclosure of Invention The invention provides a GNSS part ambiguity fixed acceleration method based on isomorphism multiplexing, which is used for solving the defects that in the GNSS high-precision real-time calculation in the prior art, high time delay is caused by calculation consumption generated by iteration ambiguity fixation and real-time application is blocked, and aiming at the defects that when the existing LAMBDA and an improved algorithm thereof sequentially pro