Unlock instant, AI-driven research and patent intelligence for your innovation.

A ground-based augmentation system for high-precision prediction of tropospheric refractivity

A ground-based enhancement system and refractive index technology, applied in the field of ground-based enhancement systems, can solve problems such as poor accuracy of the refractive index of the fluid layer.

Active Publication Date: 2020-12-18
BEIHANG UNIV
View PDF12 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] However, because the establishment time of GBAS stations in each place is different, the amount of climate data obtained is also different, and there are differences in the amount of different meteorological data. Different meteorological data affect the prediction of atmospheric refractivity, resulting in the prediction of tropospheric refractivity. poor precision

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A ground-based augmentation system for high-precision prediction of tropospheric refractivity
  • A ground-based augmentation system for high-precision prediction of tropospheric refractivity
  • A ground-based augmentation system for high-precision prediction of tropospheric refractivity

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0092] In this embodiment, a BP neural network is established to predict the future tropospheric refractivity under normal weather conditions and a large amount of meteorological data.

[0093] The data processing of the present invention mainly concentrates on the processor of ground base station to carry out, as figure 2 As shown in the flow chart of predicting the refractive index in the embodiment of the present invention, the processor includes executing the following instructions:

[0094] Step S101, acquiring meteorological data.

[0095] Call historical data from the database to obtain meteorological parameters.

[0096] The ground collection device 101 collects local meteorological data such as temperature, humidity, and air pressure, and uses the collected meteorological parameters as historical data. The processor 102 invokes the historical data collected by the ground collection equipment to obtain meteorological data such as temperature, humidity, and air press...

Embodiment 2

[0149] In this embodiment, under normal weather conditions and with less meteorological data, a BP neural network is established to predict the future tropospheric refractivity.

[0150] The difference between the first implementation in this embodiment is that the processor executes step S31 of the instruction for processing data. The processor in this embodiment executes the following instructions in detail below:

[0151] Step S101, acquiring meteorological data.

[0152] Call historical data from the database to obtain meteorological parameters. Under normal weather conditions and when the weather data is less, the training set and the test set are relatively small. In this embodiment, the two parts are no longer used as the training set and the test set, and the cross-validation method is used to expand the amount of data. .

[0153] Step S102, calculating the refractive index.

[0154] The refractive index is calculated from the acquired meteorological parameters.

...

Embodiment 3

[0202] In this embodiment, for the situation of meteorological data under abnormal weather, a BP neural network is established to predict the future tropospheric refractivity. Abnormal weather generally refers to an abnormal phenomenon with small spatial scale, short life history and obvious suddenness, or the change trend of meteorological parameters is different from the normal situation. Such as temperature inversion, short-term heavy rainfall, typhoon, high temperature, heavy fog and other weather.

[0203] According to the present invention, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the database is formed by updating the meteorological data of abnormal weather, the refractive index is calculated, and the BP neural network is established to predict the refractive index.

[0204] Specifically, such as Figure 4 The flow chart of predicting the refractive index under abnormal weather conditions in the embodiment of the present inventio...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The present invention provides a ground-based augmentation system capable of predicting a tropospheric refractive index with high precision. The system includes a ground base station and an airborne receiver. The ground base station includes a ground acquisition device, a processor, and a transmitter. The ground acquisition device is configured to acquire meteorological parameters of a plurality of years, and use the acquired meteorological parameters as historical data. The processor is configured to call the historical data and establish a back propagation (BP) neural network to predict a refractive index. The transmitter is configured to send the refractive index predicted by the processor to the airborne receiver. With the present invention, the tropospheric refractive index is predicted for different weather conditions, thus improving the precision of tropospheric refractive index prediction.

Description

technical field [0001] The invention relates to the technical field of satellite navigation, in particular to a ground-based augmentation system for high-precision prediction of tropospheric refractivity. Background technique [0002] The global satellite navigation system can provide users with all-weather, all-time, real-time, high-precision navigation and positioning services. However, there are many errors in satellite navigation and positioning, such as: satellite clock error, satellite orbit error, ionosphere error, troposphere error, receiver clock error, etc. Among them, the troposphere error will cause a maximum positioning error of 2 to 3m. [0003] The troposphere is the part of the earth's atmosphere close to the ground, where about 75% of the air mass and more than 90% of the water vapor mass are concentrated. The thickness of the troposphere varies with latitude. The atmosphere is thinner at high latitudes and thicker at low latitudes. . Many atmospheric phen...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G01S19/40
CPCG06N3/04G06N3/084G01S19/072G01S19/07G01W1/16
Inventor 王志鹏朱衍波庄园园
Owner BEIHANG UNIV