Satellite navigation and positioning method considering non-modeling errors

Abstract

The invention relates to a satellite navigation and positioning method considering non-modeling errors. According to the satellite navigation and positioning method considering non-modeling error, some non-modeling errors which are neglected in a traditional method and cannot be corrected by models and absorbed by parameters are considered, and therefore, the positioning accuracy and reliability of the method of the invention are improved compared with that of the traditional method; since the non-modeling errors have spatial and temporal correlations, a multi-epoch partial parameterization method is used to optimize observation equations enabling non-modeling error significance, and therefore, the accuracy of a model is improved; observation residuals are adopted test the significance ofthe non-modeling errors; since redundant observations are increased in a multi-frequency multi-mode GNSS application environment, the rank defect problem of the residuals can be alleviated, and therefore, the method is especially suitable for multi-frequency multi-mode GNSS observation data; and thus, the satellite navigation and positioning method considering non-modeling errors has the advantages of high precision, high reliability, wide application range and the like.

Description

technical field [0001] The invention relates to a processing method for taking into account the non-modeling errors in applications such as satellite navigation and positioning, in particular to a precise satellite navigation and positioning method taking into account the non-modeling errors. Background technique [0002] Global Navigation Satellite System (GNSS) measurements contain various types of errors, namely gross errors, random noise, and systematic errors. Without considering accidental errors and gross errors, the systematic errors that cannot be corrected by the model and absorbed by the parameters are called non-modeling errors. Unmodeled errors can affect the properties of observations. Therefore, for different application modes and observation environments, relevant studies have been carried out on errors such as colored noise, multipath effects, and atmospheric biases that have significant effects. [0003] At present, most of the research focuses on the mode...

AI Technical Summary

Problems solved by technology

Tropospheric delay is usually parametrized by zenith delay and mapping function, but due to the spatial anisotropy of tropospheric delay and the limited precision of projection function, there are significant non-modelling errors in the troposphere, which seriously affect the rapid fixation of ambiguity and high-precision positioning

The ionospheric first-order delay can be eliminated by parameterization or inter-frequency combination, but the error of the non-modeled high-order term can reach several millimeters or even several centimeters, and the change is extremely irregular in low latitude areas, which is also the main factor affecting the application of high-precision GNSS. factor

The multipath effect is closely related to the observation environment, especially the special constellation distribution characteristics of my country's Beidou satellite system, which makes it impossible to establish a reasonable and general multipath model

However, most of the existing multipath research results are based on the periodicity of satellite orbits and use stellar day filtering to process static CORS data, and there are few multipath research involving dynamic positioning.

In short, the current error refinement from the perspective of the function model is essentially limited to the error processing of specific forms of modelable parts such as multipath, troposphere and ionosphere, and there is no systematic research and control on the comprehensive quantity of these errors.

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