Adaptive genetic algorithm-based single-frequency GNSS (Global Navigation Satellite System) integer ambiguity acquisition method

Abstract

The invention discloses an adaptive genetic algorithm-based single-frequency GNSS integer ambiguity acquisition method. The method includes the following steps: step 1: acquiring the observed data of a GNSS carrier phase, and establishing a double-difference observation equation for the GNSS carrier phase; step 2: according to the double-difference observation equation obtained in step 1, utilizing the least square method to acquire the float solution and corresponding covariance matrix of GNSS integer ambiguity; step 3: utilizing known base length as a constraint condition to determine the search space of integer ambiguity; step 4: utilizing the whitening filter method to decorrelate the float solution and covariance matrix of integer ambiguity obtained in step 2; step 5: according to anobjective function, determining a fitness function, determining each operating parameter in the adaptive genetic algorithm, finally, introducing the adaptive genetic algorithm into fast solution on integer ambiguity, and searching the optimal solution of integer ambiguity.

Description

technical field [0001] The invention relates to a single-frequency GNSS integer ambiguity acquisition method based on an adaptive genetic algorithm, and belongs to the technical field of solving the single-frequency GNSS integer ambiguity by using an optimization algorithm. Background technique [0002] When using GNSS carrier phase for high-precision attitude measurement and relative positioning, the most critical thing is to quickly and accurately solve the initial integer ambiguity of the carrier phase. Single-frequency GNSS integer ambiguity solving methods can be divided into two categories: instantaneous methods and motion-based methods. The instantaneous algorithm refers to searching all possible ambiguity combinations within an epoch, and at the same time proposes that the residual error becomes too large. Candidate solutions to find the solution that minimizes the error residual. The appeal of this type of method is that it provides an "instantaneous" solution and i...

AI Technical Summary

Problems solved by technology

When the basic genetic algorithm searches for the global optimal solution, since the genetic algorithm operating parameters crossover probability and mutation probability are constant, they cannot be changed with the characteristics of individuals in the population during the search process, which makes it easy to make the initial setting The crossover probability and mutation probability cannot satisfy the search in the early and late stages of the operation, which in turn will lead to the destruction of the good model in the population, and the population is prone to premature maturity, which affects the speed and efficiency of the search

Images