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A bp neural network tropospheric delay correction method based on saastamoinen model

A BP neural network and tropospheric delay technology, applied in the field of global navigation system, can solve the problems of poor model accuracy, low model accuracy, systematic deviation of Saastamoinen model, etc., and achieve the effect of eliminating systematic deviation and high model accuracy.

Active Publication Date: 2018-02-02
SOUTHEAST UNIV
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Problems solved by technology

In a local area or using regional meteorological data, the model accuracy of this type of model is poor, especially in areas with a vast area and complex environment like our country, the correction effect is relatively limited
[0004] (2) The Saastamoinen model with meteorological parameters is a global tropospheric delay model established using North American meteorological data, so the Saastamoinen model has certain systematic deviations in China
The accuracy of the model in some areas is low and cannot meet the requirements of precise positioning

Method used

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  • A bp neural network tropospheric delay correction method based on saastamoinen model
  • A bp neural network tropospheric delay correction method based on saastamoinen model
  • A bp neural network tropospheric delay correction method based on saastamoinen model

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Embodiment Construction

[0033] The technical solution of the present invention will be further introduced below in combination with specific embodiments.

[0034] The invention discloses a BP neural network tropospheric delay correction method based on the Saastamoinen model, comprising the following steps:

[0035] S1: According to the Saastamoinen model, calculate the tropospheric wet delay value ZWD at the station SAAS , as shown in formula (1);

[0036]

[0037] in, for:

[0038]

[0039] S2: Establish a BP neural network representing the wet delay at the station, such as figure 1 As shown, the BP neural network is used to represent the nonlinear relationship between the wet delay of the station and the meteorological parameters and the wet delay of the Saastamoinen model, as follows:

[0040] The input parameters of the BP neural network are surface meteorological parameters and the wet delay calculation value ZWD of the Saastamoinen model SAAS , where the surface meteorological para...

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Abstract

The invention discloses a Saastamoinen model-based BP nerve network troposphere delay correction method. The method is characterized by comprising the following steps: S1, according to a Saastamoinen model, calculating a troposphere mostire delay value ZWDSAAS of a station; S2, establishing a BP nerve network representing a moisture delay at the station, and representing nonlinear rations between the moisture delay of the station and meteorological parameters and a Saastamoinen model moisture delay; S3, training the BP nerve network by use of high-precision IGS troposphere delay product data; S4, calculating the moisture delay at the station through the BP nerve network; and S5, calculating a troposphere zenith delay after modification. The precision of the method is quite high.

Description

technical field [0001] The invention relates to the field of global navigation systems, in particular to a Saastamoinen model-based BP neural network tropospheric delay correction method. Background technique [0002] Tropospheric delay is the main reason that affects the positioning accuracy of satellite navigation, especially the accuracy in the elevation direction. At present, the main method of tropospheric delay correction is the model correction method. The model correction method establishes a functional relationship that can reflect the tropospheric delay based on different assumptions and influencing factors. The tropospheric delay correction model is an empirical formula obtained by analyzing meteorological data, and there are differences due to different analytical methods. According to whether meteorological parameters are required for model calculation, it can be divided into models requiring meteorological parameters and models without meteorological paramete...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/02G01S19/40
CPCG01S19/40G06N3/02
Inventor 胡伍生韩伟陈永潮
Owner SOUTHEAST UNIV
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