A tropospheric error correction method, device, electronic device and storage medium

Abstract

The present invention discloses a tropospheric error correction method, device, electronic device and storage medium, the method comprising the following steps: obtaining multiple meteorological parameter data of the two grid points closest to the measuring station; obtaining the numerical value of each meteorological parameter of the measuring station; obtaining the zenith dry delay and zenith wet delay at the measuring station; constructing a deep learning model; obtaining the tropospheric dry delay residual and the tropospheric wet delay residual corresponding to the measuring station; obtaining the tropospheric corrected dry delay and wet delay corresponding to the measuring station. The present invention utilizes multiple meteorological parameters of the grid points to obtain the numerical value of each meteorological parameter of the measuring station, thereby obtaining a more accurate meteorological parameter value of the measuring station, obtains the tropospheric dry delay residual and the tropospheric wet delay residual through the deep learning model, and finally obtains the dry delay and wet delay after compensating the corrected residual; thereby correcting the tropospheric delay, reducing errors, and improving signal accuracy.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation and positioning technology, and in particular to a tropospheric error correction method, device, electronic equipment and storage medium. Background Art

[0002] The troposphere is the layer of atmosphere closest to the earth's surface. It affects the climate and atmospheric balance, and is closely related to all aspects of human life. In GNSS positioning, due to the influence of atmospheric refraction in the troposphere, when the satellite signal passes through the troposphere, the path will bend and delay. This delay is called tropospheric delay. In the satellite navigation and positioning system, tropospheric delay is one of the important error sources, which includes dry delay and wet delay. Dry delay is mainly caused by dry air components in the atmosphere (such as oxygen and nitrogen), while wet delay is caused by water vapor in the atmosphere. Tropospheric delay is an error source that cannot be ignored in navigation and positioning solutions. Its zenith direction delay can reach more than two meters, and this delay will gradually increase as the signal elevation angle decreases. The troposphere is a non-dispersive medium, and the delay is independent of the frequency of the signal. Therefore, it is impossible to eliminate it using multi-frequency combined observations, which leads to signal distortion, and the accuracy of traditional correction methods is unstable. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a tropospheric error correction method.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] A tropospheric error correction method comprises the following steps: S1, obtaining a plurality of meteorological parameter data of two grid points closest to a measuring station; S2, substituting the data obtained in step S1 into a parameter calculation model to obtain the values ​​of the meteorological parameters of the measuring station; S3, obtaining the zenith dry delay at the measuring station according to the values ​​of the meteorological parameters of the measuring station and the latitude and altitude of the measuring station. , Zenith Wet Delay ; S4, build a deep learning model and use historical data to train the deep learning model; S5, convert the latitude, longitude, altitude, annual cumulative day, , Substitute it into the deep learning model as input value to obtain the dry delay residual of the troposphere corresponding to the station and the tropospheric wet delay residual ; S6, obtain the tropospheric corrected dry delay corresponding to the station and wet delay ,in = , = .

[0006] Furthermore, when the absolute value of the latitude of the station When the angle is greater than 15° and less than 75°, the parameter calculation model is:

[0007] ;

[0008] When the absolute value of the latitude of the station When the angle is less than or equal to 15° or greater than or equal to 75°, the parameter calculation model is:

[0009] ;

[0010] is the numerical value of the meteorological parameter, is the cumulative day of the observation date; is the latitude of the station, is the latitude of the previous grid point adjacent to the station, is the latitude of the next grid point adjacent to the measuring station; is the annual average of the meteorological parameters of the previous grid point adjacent to the station;

[0011] is the annual average of the meteorological parameters of the next grid point adjacent to the station;

[0012] is the annual change value of the meteorological parameter of the previous grid point adjacent to the station;

[0013] is the annual variation value of the meteorological parameter of the next grid point adjacent to the measuring station;

[0014] is the proportionality coefficient.

[0015] Furthermore, the meteorological parameters of the station include temperature , air pressure , relative humidity , water vapor pressure , Temperature change rate , Water vapor pressure change rate .

[0016] Furthermore, the water vapor pressure at the measuring station is calculated according to the following formula: :

[0017] ; , , , , They are the first vapor pressure coefficient, the second vapor pressure coefficient, the third vapor pressure coefficient, the fourth vapor pressure coefficient and the fifth vapor pressure coefficient; , are the first water vapor pressure constant and the second water vapor pressure constant, respectively. The temperature in Celsius is absolute zero.

[0018] Further, in step S3, the zenith delay , Zenith Wet Delay Obtained by the following formula:

[0019] ;

[0020] ;

[0021] ;

[0022] is the first delay coefficient, g is the acceleration of gravity, and H is the height of the measuring station; , , are respectively a first refractive constant, a second refractive constant and a third refractive constant;

[0023] is the gravitational acceleration of the cylindrical atmosphere, is the average temperature of water vapor.

[0024] Further, Obtained according to the following formula: .

[0025] Further, step S4 specifically includes: S41, constructing a deep learning model, wherein the number of input layer nodes of the deep learning model is 6, which are the latitude, longitude, altitude, annual cumulative day, , The output layer is the tropospheric interference delay residual corresponding to the station and the tropospheric wet delay residual ; S42, select historical station data of multiple stations as sample data to train the deep learning model.

[0026] The present invention also provides a tropospheric error correction device, comprising: a parameter acquisition module, used to obtain multiple meteorological parameter data of the two grid points closest to the measuring station; a parameter calculation module, used to substitute the data obtained in step S1 into the parameter calculation model to obtain the values ​​of the meteorological parameters of the measuring station; a delay acquisition module, which obtains the zenith dry delay at the measuring station according to the values ​​of the meteorological parameters of the measuring station and the latitude and altitude of the measuring station. , Zenith Wet Delay ; Model building module, used to build a deep learning model and use historical data to train the deep learning model; delayed residual acquisition module, used to obtain the latitude, longitude, altitude, annual cumulative day, , Substitute it into the deep learning model as input value to obtain the dry delay residual of the troposphere corresponding to the station and the tropospheric wet delay residual ; Tropospheric correction module, used to obtain the tropospheric corrected dry delay corresponding to the station and wet delay .

[0027] The present invention also proposes an electronic device, which includes a processor and a memory: the memory is used to store program code and transmit the program code to the processor; the processor is used to execute the tropospheric error correction method according to instructions in the program code.

[0028] The present invention also provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the tropospheric error correction method is implemented.

[0029] The present invention has the following beneficial effects:

[0030] The multiple meteorological parameters of the grid points are used to obtain the values ​​of the meteorological parameters of the station, so as to obtain more accurate meteorological parameter values ​​of the station, providing a stable basis for subsequent data processing; finally, the dry delay residual of the troposphere is obtained through the deep learning model and the tropospheric wet delay residual , and finally obtain the dry delay after compensation and correction of the residual and wet delay ; Thereby correcting the delay in the troposphere, reducing errors and improving signal accuracy.

[0031] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0033] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0034] Figure 2 It is a schematic diagram of a deep learning model. DETAILED DESCRIPTION

[0035] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0038] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0039] Please refer to Figure 1 A tropospheric error correction method in a preferred embodiment of the present invention includes steps S1, S2, S3, S4, S5, and S6.

[0040] S1, obtain multiple meteorological parameter data of the two grid points closest to the measuring station, including the annual average and annual change of each meteorological parameter. Meteorological parameters include temperature, air pressure, relative humidity, temperature change rate, and water vapor pressure change rate.

[0041] S2, substituting the data obtained in step S1 into the parameter calculation model to obtain the values ​​of various meteorological parameters of the measuring station.

[0042] S3, according to the values ​​of the meteorological parameters of the station and the latitude and altitude of the station, obtain the zenith dry delay at the station , Zenith Wet Delay Zenith delay includes zenith dry delay and zenith wet delay. Zenith delay refers to the delay when the satellite is directly above the observation point (i.e., zenith direction) in the satellite signal propagation path.

[0043] S4, build a deep learning model and use historical data to train the deep learning model.

[0044] S5, the latitude, longitude, altitude, annual cumulative day, , Substitute it into the deep learning model as input value to obtain the dry delay residual of the troposphere corresponding to the station and the tropospheric wet delay residual .

[0045] S6, obtain the tropospheric corrected dry delay corresponding to the station and wet delay ,in = , = .

[0046] The present invention provides a tropospheric error correction method, which uses multiple meteorological parameters of grid points to obtain the values ​​of various meteorological parameters of the measuring station, thereby obtaining more accurate meteorological parameter values ​​of the measuring station, providing a stable basis for subsequent data processing; finally, the dry delay residual of the troposphere is obtained through a deep learning model and the tropospheric wet delay residual , and finally obtain the dry delay after compensation and correction of the residual and wet delay ; Thereby, the delay of the troposphere is corrected, the error is reduced, and the signal accuracy is improved. By accurately correcting the delay effect of the troposphere on the GNSS signal, the accuracy of navigation positioning is significantly improved, especially when the signal elevation angle is small, the impact of the tropospheric delay is more significant, and this method can effectively reduce this error. The accuracy of the traditional tropospheric delay correction method is unstable, and the present invention improves the stability and reliability of the correction method by constructing a deep learning model and using historical data for training. This method can obtain meteorological parameters in real time and quickly calculate the corrected tropospheric delay to meet the needs of real-time navigation and positioning. By automating the acquisition of meteorological parameters and the correction process, manual intervention is reduced and efficiency is improved. Using historical data to train a deep learning model makes the correction method more dependent on actual observation data, thereby improving the accuracy of the correction.

[0047] Specifically, when the absolute value of the latitude of the station When the angle is greater than 15° and less than 75°, the parameter calculation model is:

[0048] ;

[0049] When the absolute value of the latitude of the station When the angle is less than or equal to 15° or greater than or equal to 75°, the parameter calculation model is:

[0050] ;

[0051] is the numerical value of the meteorological parameter, is the cumulative day of the observation date; is the latitude of the station, is the latitude of the previous grid point adjacent to the station, is the latitude of the next grid point adjacent to the station is the annual average of the meteorological parameters of the previous grid point adjacent to the station; is the annual average of the meteorological parameters of the next grid point adjacent to the station; is the annual change value of the meteorological parameter of the previous grid point adjacent to the station; is the annual variation value of the meteorological parameter of the next grid point adjacent to the measuring station; is the latitude proportionality factor, specifically, .

[0052] Substituting the meteorological parameter data of S1 into the calculation model, the numerical values ​​of the meteorological parameters of the corresponding measuring station can be obtained. , , and The parameter type and data of the meteorological parameters to be calculated correspond to each other. For example, when the temperature at the measuring station needs to be calculated, That is, the annual average temperature of the previous grid point adjacent to the station. is the annual change of the temperature of the previous grid point adjacent to the station. If you need to find a parameter, just substitute the station data of the corresponding parameter.

[0053] The temperature at the measuring station can be calculated through the parameter calculation model , air pressure , relative humidity , Temperature change rate , Water vapor pressure change rate The meteorological parameters of the station include temperature , air pressure , relative humidity , water vapor pressure , Temperature change rate , Water vapor pressure change rate Therefore, it is also necessary to obtain the water vapor pressure at the measuring station The water vapor pressure at the measuring station can be calculated according to the following formula :

[0054] .

[0055] , , , , They are the first vapor pressure coefficient, the second vapor pressure coefficient, the third vapor pressure coefficient, the fourth vapor pressure coefficient and the fifth vapor pressure coefficient; , are respectively the first water vapor pressure constant and the second water vapor pressure constant. In this embodiment, , , , , , , , The Celsius temperature value at absolute zero is . exp refers to the exponential with the natural number e as the base.

[0056] In step S3, the zenith delay , Zenith Wet Delay Obtained by the following formula:

[0057] ;

[0058] ;

[0059] .

[0060] is the first delay coefficient. In this embodiment, ; g is the acceleration due to gravity, H is the height of the measuring station; , , are the first refractive constant, the second refractive constant and the third refractive constant respectively. In this embodiment, , , The values ​​are 77.60K / mbar, 16K / mbar, 377600K / mbar; is the gravitational acceleration of the cylindrical atmosphere, is the average temperature of water vapor.

[0061] Specifically, It can be obtained according to the following formula:

[0062] .

[0063] Step S4 specifically includes S41 and S42.

[0064] S41, construct a deep learning model, the input layer node number of the deep learning model is 6, which are the latitude, longitude, altitude, annual cumulative day, , The output layer is the tropospheric interference delay residual corresponding to the station and the tropospheric wet delay residual ; Tropospheric dry delay residual and the tropospheric wet delay residual The calculation formula is as follows: = , = .

[0065] S42, select historical station data of multiple stations as sample data to train the deep learning model. The station sites are preferentially selected from the target station and the stations near the target station, so as to minimize the impact of environmental factors. Specifically, the historical station data can be selected from the GGOS tropospheric data of the past five years.

[0066] The deep learning model can use the LSTM model. LSTM can extract features from the input time series according to the time dimension, using the latitude, longitude, altitude, annual accumulation day, and A total of 6 feature dimensions are used as input data of the LSTM model, and the output feature is the tropospheric dry delay residual at the station. and the tropospheric wet delay residual During training, the station latitude, longitude, altitude, annual cumulative day, , , , , and Historical data are used.

[0067] LSTM unit such as Figure 2 As shown, the input of an LSTM unit includes the memory cell state , the unit output at the last moment and the current feature input . In the structure For the Gate of Oblivion; The sigmoid activation function maps any value to the range of (0, 1); For the input gate, is the state candidate quantity, is the output gate, is the cell state at the current moment, is the unit output at the current moment. The calculation of sigmoid function and tanh function is:

[0068]

[0069]

[0070] The number of LSTM iterations is set to 1000, the step size is set to 100, and the learning rate is set to 0.01.

[0071] The above model is trained using sample data, and the residual prediction model of the model is constructed through continuous updating and iteration. The cross entropy loss function is used to improve the prediction accuracy. The cross entropy loss function is defined as follows:

[0072]

[0073] in, It is The true label of the sample The value of the category, The model predicts The sample The probability of a class.

[0074] in, It is The true label of the sample The value of the category, The model predicts The sample The probability of each category. N is the number of samples and K is the number of categories.

[0075] The present invention also provides a tropospheric error correction device, comprising a parameter acquisition module, a parameter calculation module, a delay acquisition module, a model construction module, a delay residual acquisition module and a tropospheric correction module.

[0076] The parameter acquisition module is used to obtain multiple meteorological parameter data of the two grid points closest to the measuring station; the parameter calculation module is used to substitute the data obtained in step S1 into the parameter calculation model to obtain the values ​​of the meteorological parameters of the measuring station; the delay acquisition module obtains the zenith dry delay at the measuring station according to the values ​​of the meteorological parameters of the measuring station and the latitude and altitude of the measuring station. , Zenith Wet Delay ; Model building module, used to build a deep learning model and use historical data to train the deep learning model; delayed residual acquisition module, used to obtain the latitude, longitude, altitude, annual cumulative day, , Substitute it into the deep learning model as input value to obtain the dry delay residual of the troposphere corresponding to the station and the tropospheric wet delay residual ; Tropospheric correction module, used to obtain the tropospheric corrected dry delay corresponding to the station and wet delay .

[0077] The present invention also proposes an electronic device, which includes a processor and a memory: the memory is used to store program code and transmit the program code to the processor; the processor is used to execute a tropospheric error correction method according to instructions in the program code.

[0078] The present invention also provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the tropospheric error correction method is implemented.

[0079] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A tropospheric error correction method, characterized in that: The steps include: S1, obtain multiple meteorological parameter data of the two grid points closest to the measuring station; S2, substituting the data obtained in step S1 into the parameter calculation model to obtain the values ​​of each meteorological parameter of the measuring station; S3, according to the values ​​of the meteorological parameters of the station and the latitude and altitude of the station, obtain the zenith dry delay at the station , Zenith Wet Delay ; S4, build a deep learning model and use historical data to train the deep learning model; S5, the latitude, longitude, altitude, annual cumulative day, , Substitute it into the deep learning model as input value to obtain the dry delay residual of the troposphere corresponding to the station and the tropospheric wet delay residual ; S6, obtain the tropospheric corrected dry delay corresponding to the station and wet delay ,in = , = ; When the absolute value of the latitude of the station When the angle is greater than 15° and less than 75°, the parameter calculation model is: ； When the absolute value of the latitude of the station When the angle is less than or equal to 15° or greater than or equal to 75°, the parameter calculation model is: ； is the numerical value of the meteorological parameter, is the annual cumulative day of the observation date; is the latitude of the station, is the latitude of the previous grid point adjacent to the station, is the latitude of the next grid point adjacent to the measuring station; is the annual average of the meteorological parameters of the previous grid point adjacent to the station; is the annual average of the meteorological parameters of the next grid point adjacent to the station; is the annual change value of the meteorological parameter of the previous grid point adjacent to the station; is the annual variation value of the meteorological parameter of the next grid point adjacent to the measuring station; is the proportionality coefficient, ; The meteorological parameters of the station include temperature , air pressure , relative humidity , Vapor Pressure , Temperature change rate , Water vapor pressure change rate ; The water vapor pressure at the measuring station is calculated according to the following formula : ； , , , , They are the first vapor pressure coefficient, the second vapor pressure coefficient, the third vapor pressure coefficient, the fourth vapor pressure coefficient and the fifth vapor pressure coefficient; , are the first water vapor pressure constant and the second water vapor pressure constant, respectively. The temperature in Celsius is absolute zero.

2. The tropospheric error correction method according to claim 1, characterized in that: In step S3, the zenith delay , Zenith Wet Delay Obtained by the following formula: ； ； ； is the first delay coefficient, g is the acceleration of gravity, and H is the height of the measuring station; , , are respectively a first refractive constant, a second refractive constant and a third refractive constant; is the gravitational acceleration of the cylindrical atmosphere, is the average temperature of water vapor.

3. The tropospheric error correction method according to claim 2, characterized in that: Obtained according to the following formula: 。 4. The tropospheric error correction method according to claim 1, characterized in that: Step S4 specifically includes: S41, construct a deep learning model, the input layer node number of the deep learning model is 6, which are the latitude, longitude, altitude, annual cumulative day, , The output layer is the tropospheric interference delay residual corresponding to the station and the tropospheric wet delay residual ; S42, selecting historical station data of multiple stations as sample data to train the deep learning model.

5. A tropospheric error correction device, used to implement the tropospheric error correction method according to any one of claims 1 to 4, characterized in that: include: A parameter acquisition module is used to obtain multiple meteorological parameter data of the two grid points closest to the measuring station; A parameter calculation module is used to substitute the data obtained in step S1 into the parameter calculation model to obtain the values ​​of various meteorological parameters of the measuring station; The delay acquisition module obtains the zenith dry delay at the station based on the values ​​of the meteorological parameters of the station and the latitude and altitude of the station. , Zenith Wet Delay ; Model building module, used to build deep learning models and train them using historical data; The delayed residual acquisition module is used to obtain the latitude, longitude, altitude, annual cumulative day, , Substitute it into the deep learning model as input value to obtain the dry delay residual of the troposphere corresponding to the station and the tropospheric wet delay residual ; Tropospheric correction module, used to obtain the tropospheric corrected dry delay corresponding to the station and wet delay .

6. An electronic device, characterized in that: including a processor and a memory, The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the tropospheric error correction method according to any one of claims 1-4 according to the instructions in the program code.

7. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the tropospheric error correction method according to any one of claims 1 to 4 is implemented.

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