阅读说明：本技术 用于gnss数据处理的自适应对流层延迟改正方法及系统 (Adaptive troposphere delay correction method and system for GNSS data processing ) 是由 刘小丁 周建营 陈国恒 张惠军 朱紫阳 董斌斌 张文峰 华水胜 于 2020-05-29 设计创作，主要内容包括：本发明公开了一种用于GNSS数据处理的自适应对流层延迟改正方法及系统,所述方法包括获取工程区域测站的同步观测数据,判断是否进行对流层延迟估计；若否,将同步观测数据作为第一数据处理结果；若是,则重复执行对流层延迟改正模型运算、参数估计、对流层映射函数改正及指标分析处理,对比后取较优结果的步骤,直至筛选出对流层延迟估计的最优结果作为第二数据处理结果；最后将第一数据处理结果与第二数据处理结果进行最优解分析,得到对流层延迟数据处理的最优结果。本发明提供的用于GNSS数据处理的自适应对流层延迟改正方法,采用不同对流层延迟改正模型、对流层映射函数及参数估计,通过自适应筛选获得数据处理的最优解,提高了处理结果的精度。(The invention discloses a self-adaptive troposphere delay correction method and a self-adaptive troposphere delay correction system for GNSS data processing, wherein the method comprises the steps of obtaining synchronous observation data of a survey station in an engineering area and judging whether troposphere delay estimation is carried out or not; if not, taking the synchronous observation data as a first data processing result; if so, repeatedly executing the tropospheric delay correction model operation, parameter estimation, tropospheric mapping function correction and index analysis processing, and comparing and then taking a better result until the optimal result of the tropospheric delay estimation is screened out as a second data processing result; and finally, performing optimal solution analysis on the first data processing result and the second data processing result to obtain an optimal result of tropospheric delay data processing. The adaptive troposphere delay correction method for GNSS data processing provided by the invention adopts different troposphere delay correction models, troposphere mapping functions and parameter estimation, obtains the optimal solution of data processing through adaptive screening, and improves the precision of processing results.)

1. An adaptive tropospheric delay correction method for GNSS data processing, comprising:

acquiring synchronous observation data of a survey station in an engineering area, and judging whether troposphere delay estimation is carried out or not according to the synchronous observation data;

if not, taking the synchronous observation data as a first data processing result;

if so, repeatedly executing the following steps until the optimal result of the tropospheric delay estimation is screened out to be used as a second data processing result:

inputting the result after the estimation of the troposphere delay into a troposphere delay correction model for operation; the models include a hopfield model, a Sasta morningine model and a Brookfield model;

performing parameter estimation on the operated result by a piecewise linearity method and a least square method;

inputting the result after parameter estimation into a troposphere mapping function for correction; the troposphere mapping function is included in the GMF, NMF and VMF1 functions;

sequentially processing the corrected result by a standardized mean square deviation value, baseline repeatability and each baseline component precision, screening out a result of which the standardized mean square deviation value is less than 0.3, comparing the baseline repeatability of the data with each baseline component precision, and taking a better result;

and performing optimal solution analysis on the first data processing result and the second data processing result according to the distance, the altitude difference, the climate condition and the observation environmental factors among the stations in the engineering area to obtain the optimal result of the tropospheric delay data processing.

2. The adaptive tropospheric delay correction method for GNSS data processing of claim 1, characterized in that the hopkindred model formula is:

wherein, Δ S_d、ΔS_wM is taken as a unit, d and w are respectively a troposphere dry steam part and a moisture part, the air pressure P and the water air pressure E are respectively taken as a unit of millibar (mbar), the air temperature T is taken as a unit of degree, the absolute temperature is adopted, and the altitude angle E is taken as a unit of degree;

the Sasta Morinin model formula is as follows:

wherein Δ S is in units of m, the elevation angle E is in units of degrees, the gas pressure P and the water pressure E are both in units of millibars (mbar),whereinThe latitude of the survey station; h is_sElevation of the survey station (in km); b is h_sA list function of; r is E and h_sA list function of; p_S、T_S、e_sIs a meteorological element on the survey station; the above formula can be expressed by numerical fitting:

the blanc model formula is:

wherein the elevation angle E is in degrees and R is_sFor measuring the distance from the station to the center of the earth,/₀And b (E) as path correction parameters, d_hAnd d_wRespectively, zenith dry and wet delays.

3. The adaptive tropospheric delay correction method for GNSS data processing of claim 1, characterized in that the parameter estimation of the computed result by a piecewise linear method and a least squares method is specifically:

performing parameter estimation by using a piecewise linear method, taking troposphere zenith delay as an unknown parameter, and using a step length of K delta_tThe discrete random process of (a) represents the change of tropospheric delay over time as follows:

and assuming that the tropospheric refraction in the zenith direction of the survey station varies linearly with time between epochs I to I + K as follows:

k is selected to reduce the number of refraction parameters rho (I) and rho (I + K) of the troposphere in the zenith direction of the observation station to be solved, and then a least square method is used for estimating the parameters;

and estimating parameters according to the time interval of N hours according to the time variation relation of the troposphere delay with time and the linear time variation relation of the troposphere refraction, wherein N is a positive integer greater than 0.

4. The adaptive tropospheric delay correction method for GNSS data processing of claim 1, characterized in that the results after parameter estimation are input to tropospheric mapping functions for correction; the troposphere mapping function is included in the GMF, NMF and VMF1 functions, including:

selecting a troposphere mapping function according to the relationship between the troposphere delay STD and the troposphere zenith delay ZTD of the observation station, wherein the relationship conforms to the formula: STD-m × ZTD, where m is a mapping function;

the NMF includes a dry component projection function m_dAnd a moisture component projection function m_w(ii) a The dry component projection function m_dThe expression of (a) is:

wherein E is a height angle; a is_ht＝2.53×10^-5；b_ht＝5.49×10^-3；c_ht＝1.14×10^-3(ii) a H is positive high, where the coefficient a_d、b_d、c_dAt a survey station latitude of between 15 ° and 75 °, the value is obtained by interpolation using the following formula:

in the formula, p represents a coefficient a to be interpolated_d、b_d、c_d(ii) a t is the cumulative year; t is t₀28 is the annual date of the reference moment;

wherein a is_d、b_d、c_dThe calculation formula of the equal coefficient when the latitude of the measuring station is less than 15 degrees is as follows:

a_d、b_d、c_dthe calculation formula of the equal coefficient when the latitude of the measuring station is more than 75 degrees is as follows:

projection function m of the wet component_wThe expression of (a) is:

in the formula, the coefficient a_w、b_w、c_wAt the measuring station latitude between 15 degrees and 75 degrees, the latitude is obtained by interpolation according to the following formula:

wherein, when the latitude of the measuring station is less than 15 degrees, the value p at 15 degrees is taken_avg(ii) a When the latitude of the measuring station is more than 75 degrees, the value p at 75 degrees is taken_avg。

5. The adaptive tropospheric delay correction method for GNSS data processing of claim 4, characterized in that,

the dry and wet component projection functions of VMF1 are different from the dry and wet component projection functions of NMF in a_d、b_d、c_dAnd a_w、b_w、c_wThe values of (A) are different; a of the VMF1_dAnd a_wThe coefficients are provided by a grid graph generated from measured meteorological data: the warp difference is 2.5 degrees, the weft difference is 2 degrees and the time interval is 6 hours; b_dTaking 0.0029 as a constant; c. C_dThen the following equation is used to calculate:

in the formula, b_wAnd c_wTaking a constant value, wherein b_w＝0.00146，c_w＝0.04391，c₀、c₁₁、c₁₀Is a constant;

the dry and wet component projection functions of the GMF are different from the dry and wet component projection functions of the VMF1 in that the coefficient a_dAnd a_wLatitude and longitude of station expressed as DAY of year DAYAnd elevation H, said coefficient a_dAnd a_wThe calculation method is the same, and the expression is as follows:

in the formula, a₀Is the average value, A is the amplitude; the calculation is obtained by expanding to 9 th order through a spherical harmonic function expression:

6. the adaptive tropospheric delay correction method for GNSS data processing of claim 1, characterized in that the formula of the normalized mean square error value is:

in the formula, N is the number of the stations; y is_iBaseline side length for day i; y is a weighted average of the side lengths of the single-day solution baselines;is the error in the unit weight;

the baseline repeatability formula is:

in the formula, n is the total number of observation periods of the same baseline; c_iA certain component or side length of a baseline for a period of time;

7. The adaptive tropospheric delay correction method for GNSS data processing of claim 1 wherein the synchronized observation data comprises starting point or provincial continuously operating reference station data.

8. A system for adaptive tropospheric delay correction for GNSS data processing, comprising:

the data judgment unit is used for acquiring synchronous observation data of a survey station in a project area and judging whether troposphere delay estimation is carried out or not according to the synchronous observation data; if not, taking the synchronous observation data as a first data processing result; if so, repeating the steps executed by the loop processing unit until the optimal result of tropospheric delay estimation is screened out to be used as a second data processing result;

the circulation processing unit is used for inputting the result after the estimation of the troposphere delay into the troposphere delay correction model for operation; the models include a hopfield model, a Sasta morningine model and a Brookfield model; performing parameter estimation on the operated result by a piecewise linearity method and a least square method; inputting the result after parameter estimation into a troposphere mapping function for correction; the troposphere mapping function is included in the GMF, NMF and VMF1 functions; sequentially processing the corrected result by a standardized mean square deviation value, baseline repeatability and each baseline component precision, screening out a result of which the standardized mean square deviation value is less than 0.3, comparing the baseline repeatability of the data with each baseline component precision, and taking a better result;

and the optimal result acquisition unit is used for performing optimal solution analysis on the first data processing result and the second data processing result according to the distance, the altitude difference, the climate condition and the observation environmental factors among the engineering area measuring stations to obtain the optimal result of the troposphere delay data processing.

9. A computer terminal device, comprising:

one or more processors;

a memory coupled to the processor for storing one or more programs;

when executed by the one or more processors, cause the one or more processors to implement the adaptive tropospheric delay correction method for GNSS data processing of any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out an adaptive tropospheric delay correction method for GNSS data processing according to any one of claims 1 to 7.

Technical Field

The invention relates to the technical field of satellite navigation and positioning, in particular to a self-adaptive troposphere delay correction method and system for GNSS data processing.

Background

Tropospheric delay is one of the main error sources for global navigation satellite system positioning, and conventional tropospheric delay correction methods at present include a model correction method, a parameter estimation method and an external correction method. The model correction method comprises the steps of firstly constructing a model, and then projecting the model to the direction of a signal propagation path by using a corresponding mapping function for correction; however, under the condition of uneven water vapor distribution, the selection of the model is greatly influenced in the zenith direction and the low altitude angle direction, and particularly in coastal areas with frequent water vapor transmission and exchange, the data processing results are different due to different model selections. The parameter estimation method processes data by taking troposphere delay zenith delay as an unknown parameter, but the calculation processes of parameter value setting and trial calculation screening are complicated and long in time consumption. The external correction method obtains zenith delay correction quantity of each survey station of the GNSS network through measurement and calculation of instrument equipment, can effectively improve data processing precision, but has high requirements on the instrument equipment and observation conditions, so that field measurement cost is high, and feasibility in practical engineering application is low.

Disclosure of Invention

The invention aims to provide a self-adaptive troposphere delay correction method and a self-adaptive troposphere delay correction system for GNSS data processing, which comprehensively consider whether troposphere delay estimation is added or not, adopt different troposphere delay correction empirical models, troposphere mapping functions and selection of troposphere delay parameter estimation aiming at different conditions to analyze various precision indexes, obtain an optimal solution of data processing through self-adaptive screening and improve the precision of a processing result.

In order to overcome the defects in the prior art, an embodiment of the present invention provides an adaptive troposphere delay correction method for GNSS data processing, including:

acquiring synchronous observation data of a survey station in an engineering area, and judging whether troposphere delay estimation is carried out or not according to the synchronous observation data;

if not, taking the synchronous observation data as a first data processing result;

if so, repeatedly executing the following steps until the optimal result of the tropospheric delay estimation is screened out to be used as a second data processing result:

inputting the result after the estimation of the troposphere delay into a troposphere delay correction model for operation; the models include a hopfield model, a Sasta morningine model and a Brookfield model;

performing parameter estimation on the operated result by a piecewise linearity method and a least square method;

inputting the result after parameter estimation into a troposphere mapping function for correction; the troposphere mapping function is included in the GMF, NMF and VMF1 functions;

sequentially processing the corrected result by a standardized mean square deviation value, baseline repeatability and each baseline component precision, screening out a result of which the standardized mean square deviation value is less than 0.3, comparing the baseline repeatability of the data with each baseline component precision, and taking a better result;

and performing optimal solution analysis on the first data processing result and the second data processing result according to the distance, the altitude difference, the climate condition and the observation environmental factors among the stations in the engineering area to obtain the optimal result of the tropospheric delay data processing.

Preferably, the hopfield model formula is:

wherein, Δ S_d、ΔS_wM is taken as a unit, d and w are respectively a troposphere dry steam part and a moisture part, the air pressure P and the water air pressure E are respectively taken as a unit of millibar (mbar), the air temperature T is taken as a unit of degree, the absolute temperature is adopted, and the altitude angle E is taken as a unit of degree;

the Sasta Morinin model formula is as follows:

wherein Δ S is in units of m, the elevation angle E is in units of degrees, the gas pressure P and the water pressure E are both in units of millibars (mbar),whereinThe latitude of the survey station; h is_sElevation of the survey station (in km); b is h_sA list function of; r is E and h_sA list function of; p_S、T_S、e_sIs a meteorological element on the survey station; the above formula can be expressed as after numerical fitting：

The blanc model formula is:

wherein the elevation angle E is in degrees and R is_sFor measuring the distance from the station to the center of the earth,/₀And b (E) as path correction parameters, d_hAnd d_wRespectively, zenith dry and wet delays.

Preferably, the performing parameter estimation on the result after the operation by using a piecewise linearity method and a least square method specifically includes:

performing parameter estimation by using a piecewise linear method, taking troposphere zenith delay as an unknown parameter, and using a step length of K delta_tThe discrete random process of (a) represents the change of tropospheric delay over time as follows:

and assuming that the tropospheric refraction in the zenith direction of the survey station varies linearly with time between epochs I to I + K as follows:

k is selected to reduce the number of refraction parameters rho (I) and rho (I + K) of the troposphere in the zenith direction of the observation station to be solved, and then a least square method is used for estimating the parameters;

and estimating parameters according to the time interval of N hours according to the time variation relation of the troposphere delay with time and the linear time variation relation of the troposphere refraction, wherein N is a positive integer greater than 0.

Preferably, the result after parameter estimation is input to the troposphere mapping function for correction; the troposphere mapping function is included in the GMF, NMF and VMF1 functions, including:

selecting a troposphere mapping function according to the relationship between the troposphere delay STD and the troposphere zenith delay ZTD of the observation station, wherein the relationship conforms to the formula: STD-m × ZTD, where m is a mapping function;

the NMF includes a dry component projection function m_dAnd a moisture component projection function m_w(ii) a The dry component projection function m_dThe expression of (a) is:

wherein E is a height angle; a is_ht＝2.53×10^-5；b_ht＝5.49×10^-3；c_ht＝1.14×10^-3(ii) a H is positive high, where the coefficient a_d、b_d、c_dAt a survey station latitude of between 15 ° and 75 °, the value is obtained by interpolation using the following formula:

in the formula, p represents a coefficient a to be interpolated_d、b_d、c_d(ii) a t is the cumulative year; t is t₀28 is the annual date of the reference moment;

wherein a is_d、b_d、c_dThe calculation formula of the equal coefficient when the latitude of the measuring station is less than 15 degrees is as follows:

a_d、b_d、c_dthe calculation formula of the equal coefficient when the latitude of the measuring station is more than 75 degrees is as follows:

projection function m of the wet component_wThe expression of (a) is:

in the formula, the coefficient a_w、b_w、c_wAt the measuring station latitude between 15 degrees and 75 degrees, the latitude is obtained by interpolation according to the following formula:

wherein, when the latitude of the measuring station is less than 15 degrees, the value p at 15 degrees is taken_avg(ii) a When the latitude of the measuring station is more than 75 degrees, the value p at 75 degrees is taken_avg。

Preferably, the dry and wet component projection functions of VMF1 are different from the dry and wet component projection functions of NMF in a_d、b_d、c_dAnd a_w、b_w、c_wThe values of (A) are different; a of the VMF1_dAnd a_wThe coefficients are provided by a grid graph generated from measured meteorological data: the warp difference is 2.5 degrees, the weft difference is 2 degrees and the time interval is 6 hours; b_dTaking 0.0029 as a constant; c. C_dThen the following equation is used to calculate:

in the formula, b_wAnd c_wTaking a constant value, wherein b_w＝0.00146，c_w＝0.04391，c₀、c₁₁、c₁₀Is a constant;

the dry and wet component projection functions of the GMF are different from the dry and wet component projection functions of the VMF1 in that the coefficient a_dAnd a_wLatitude and longitude of station expressed as DAY of year DAYAnd elevation H, said coefficient a_dAnd a_wThe calculation method is the same, and the expression is as follows:

in the formula, a₀Is the average value, A is the amplitude; the calculation is obtained by expanding to 9 th order through a spherical harmonic function expression:

preferably, the formula of the normalized mean square error value is:

in the formula, N is the number of the stations; y is_iBaseline side length for day i; y is a weighted average of the side lengths of the single-day solution baselines;

is the error in the unit weight;

the baseline repeatability formula is:

in the formula, n is the total number of observation periods of the same baseline; c_iA certain component or side length of a baseline for a period of time;

for the time period i corresponds to C_iThe variance of the components; for each time interval C_mIs calculated as the weighted average of (a).

Preferably, the synchronized observation data comprises starting point or provincial continuous operation reference station data.

The embodiment of the invention also provides a system for adaptive troposphere delay correction of GNSS data processing, which comprises:

the data judgment unit is used for acquiring synchronous observation data of a survey station in a project area and judging whether troposphere delay estimation is carried out or not according to the synchronous observation data; if not, taking the synchronous observation data as a first data processing result; if so, repeating the steps executed by the loop processing unit until the optimal result of tropospheric delay estimation is screened out to be used as a second data processing result;

the circulation processing unit is used for inputting the result after the estimation of the troposphere delay into the troposphere delay correction model for operation; the models include a hopfield model, a Sasta morningine model and a Brookfield model; performing parameter estimation on the operated result by a piecewise linearity method and a least square method; inputting the result after parameter estimation into a troposphere mapping function for correction; the troposphere mapping function is included in the GMF, NMF and VMF1 functions; sequentially processing the corrected result by a standardized mean square deviation value, baseline repeatability and each baseline component precision, screening out a result of which the standardized mean square deviation value is less than 0.3, comparing the baseline repeatability of the data with each baseline component precision, and taking a better result;

and the optimal result acquisition unit is used for performing optimal solution analysis on the first data processing result and the second data processing result according to the distance, the altitude difference, the climate condition and the observation environmental factors among the engineering area measuring stations to obtain the optimal result of the troposphere delay data processing.

An embodiment of the present invention further provides a computer terminal device, including:

one or more processors;

a memory coupled to the processor for storing one or more programs;

when executed by the one or more processors, cause the one or more processors to implement an adaptive tropospheric delay correction method for GNSS data processing as described in the embodiments above.

Embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the adaptive tropospheric delay correction method for GNSS data processing according to the above embodiments.

Compared with the prior art, the invention at least has the following beneficial effects:

(1) the method integrates various model correction and parameter estimation methods, adopts iterative cycle adaptive data processing and screening, analyzes to obtain an optimal baseline data processing result, is not limited to a certain specific area, and is suitable for data processing of high-precision GNSS networks in any area (particularly coastal areas with frequent tropospheric activity), any climate characteristic and observation environment.

(2) The method solves the complicated GNSS data processing process of a surveying worker, can simply and efficiently complete troposphere delay correction data processing work of a GNSS network, improves the automatic resolving capability of GNSS data processing, and solves the technical problem of complicated and fussy data processing flow in actual engineering application.

Drawings

Fig. 1 is a flow chart illustrating an adaptive tropospheric delay correction method for GNSS data processing according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a long baseline GNSS network in a test area according to an embodiment of the present invention;

fig. 3 is a schematic diagram of a short baseline GNSS network link in a test area according to an embodiment of the present invention;

FIG. 4 is a chart illustrating N-direction baseline accuracy statistics for long baseline GNSS network data processing in a trial area, according to an embodiment of the present invention;

FIG. 5 is a direction baseline accuracy statistical chart for processing E direction baseline data of a long baseline GNSS network in a test area according to an embodiment of the present invention;

fig. 6 is a U-direction baseline accuracy statistical chart of data processing of a long baseline GNSS network in a test area according to an embodiment of the present invention;

FIG. 7 is a chart of N-direction baseline accuracy statistics for data processing of a short baseline GNSS network in a trial area according to an embodiment of the present invention;

fig. 8 is a direction baseline accuracy statistical chart of data processing E of a short baseline GNSS network in a test area according to an embodiment of the present invention;

fig. 9 is a U-direction baseline accuracy statistical chart of data processing of a short baseline GNSS network in a test area according to an embodiment of the present invention;

fig. 10 is a schematic structural diagram of an adaptive tropospheric delay correction system for GNSS data processing according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.

It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.

Referring to fig. 1, an embodiment of the present invention provides an adaptive tropospheric delay correction method for GNSS data processing, including:

s1, acquiring synchronous observation data of a survey station in the engineering area, and judging whether troposphere delay estimation is carried out or not according to the synchronous observation data;

if not, taking the synchronous observation data as a first data processing result;

if so, repeating the steps S2-S5 until the optimal result of the tropospheric delay estimation is screened out as the second data processing result.

It is understood that the earth's troposphere, which is located in the lowest layer of the atmosphere, concentrates about 75% of the atmospheric mass and more than 90% of the water vapor mass, with its lower bound being connected to the ground and its upper bound varying in height with geographical latitude and season. As one of the main error sources of global navigation satellite system positioning, the tropospheric delay is usually estimated by directly using one of a model correction method, a parameter estimation method, an external correction method, and the like, but the determination as to whether the tropospheric delay needs to be estimated is usually not performed, so that some unnecessary time may be wasted in data processing.

In the step, firstly, synchronous observation data of a survey station in a project area are obtained, and then whether troposphere delay estimation is carried out or not is judged according to the synchronous observation data; if the estimation is not needed, the synchronous observed data is directly used as the first data processing result for the optimal solution analysis in the following step S6 without any processing; if the estimation is needed, the steps S2-S5 are sequentially performed, in step S5, an intermediate better result is obtained according to the two indexes of the baseline repeatability and the accuracy of each baseline component, and then, according to the result, the steps S2-S5 are repeatedly performed until the optimal result of the tropospheric delay estimation is screened out to be used as a second data processing result for the optimal solution analysis in step S6.

In addition, synchronous observation refers to that N (N is more than or equal to 2) GPS receivers are used, and signals of the same satellite group are continuously tracked and received in the same time period; the data selected in this step mainly includes: the starting point or province that needs to be used runs the reference station data continuously.

S2, inputting the result of the estimation of the troposphere delay into a troposphere delay correction model for operation; the models include a hopfield model, a Sasta morningine model and a Brookfield model;

in the step, the three models can well accord with the troposphere delay correction in the zenith direction, wherein the correction effect of a Sastamonine model (Saastamoinen) and a Hopfield model (Hopfield) is better; it should be noted that the Hopfield model (Hopfield) is an artificial neuron network model, and includes both discrete and continuous models. Meanwhile, the discrete hopfield model is a discrete time network and is composed of n neurons.

S3, performing parameter estimation on the operated result through a piecewise linearity method and a least square method;

it should be noted that the nonlinear characteristic curve is divided into several sections, and each section is approximately replaced by a straight line segment, and this processing method is called piecewise linearization. After the piecewise linearization process, the nonlinear system under study is approximately equivalent to a linear system in each section, and the theory and method of the linear system can be adopted for analysis. The analysis results of each section, such as transition process curves or phase tracks, are connected in time sequence, namely the analysis results of the nonlinear system under study are obtained by a piecewise linearization method. The analysis accuracy and the calculation complexity of the piecewise linearization method depend on the degree of nonlinearity of the system. For nonlinear characteristics with broken line shapes, such as relay type nonlinearity and dead zone nonlinearity, the piecewise linearization method does not introduce analysis errors and does not increase complexity in calculation; for a system with low non-linearity degree, the piecewise linearization method has a better analysis result. For systems with a high degree of non-linearity, in principle, piecewise linearization methods are still applicable, but the computational complexity increases, while the accuracy of the analysis depends on how many sections are linearized.

Least squares (also known as the least squares method) is a mathematical optimization technique. It finds the best functional match of the data by minimizing the sum of the squares of the errors. Unknown data can be easily obtained by the least square method, and the sum of squares of errors between these obtained data and actual data is minimized. The least squares method can also be used for curve fitting. Other optimization problems may also be expressed in a least squares method by minimizing energy or maximizing entropy.

S4, inputting the result after parameter estimation to a troposphere mapping function for correction; the troposphere mapping function is included in the GMF, NMF and VMF1 functions;

GMF (global mapping function), which is similar in form to NMF (denier mapping function). The model is established on the basis of a VMF1 (Vienna mapping function) model, by taking the modeling idea of NMF as reference, the annual product date, longitude, latitude and elevation are taken as input parameters, an experience grid list file is established by each coefficient of the model, interpolation is carried out according to an interpolation function related to the annual product date to obtain a corresponding model coefficient value, the coefficient of the GMF only needs the position of a station to be measured and the annual product date by expanding the parameter of the VMF1 into global harmonic surface grid data.

S5, sequentially processing the corrected result by a standardized mean square deviation value, baseline repeatability and each baseline component precision, screening out a result of which the standardized mean square deviation value is less than 0.3, comparing the baseline repeatability of the data with each baseline component precision, and taking a better result;

in the step, the result corrected by the troposphere mapping function in the last step is processed by the normalized mean square deviation value, the baseline repeatability and the precision of each baseline component, the data with the normalized mean square deviation value smaller than 0.3 is firstly screened out, then the two indexes of the baseline repeatability and the precision of each baseline component are synthesized, a better result is selected out, and then the steps S2-S5 are repeatedly executed until the optimal result of troposphere delay estimation is screened out to be used as a second data processing result.

And S6, performing optimal solution analysis on the first data processing result and the second data processing result according to the distance, the altitude difference, the climate condition and the observation environmental factors among the engineering area measuring stations to obtain the optimal result of the troposphere delay data processing.

In the embodiment of the present invention, the hopfield model formula is:

wherein, Δ S_d、ΔS_wM is taken as a unit, d and w are respectively a troposphere dry steam part and a moisture part, the air pressure P and the water air pressure E are respectively taken as a unit of millibar (mbar), the air temperature T is taken as a unit of degree, the absolute temperature is adopted, and the altitude angle E is taken as a unit of degree;

the Sasta Morinin model formula is as follows:

wherein Δ S is in units of m, the elevation angle E is in units of degrees, the gas pressure P and the water pressure E are both in units of millibars (mbar),whereinThe latitude of the survey station; h is_sElevation of the survey station (in km); b is h_sA list function of; r is E and h_sA list function of; p_S、T_S、e_sIs a meteorological element on the survey station; the above formula can be expressed by numerical fitting:

the blanc model formula is:

wherein the elevation angle E is in degrees and R is_sFor measuring the distance from the station to the center of the earth,/₀And b (E) as path correction parameters, d_hAnd d_wRespectively, zenith dry and wet delays.

In the embodiment of the present invention, the performing parameter estimation on the calculated result by using a piecewise linear method and a least square method specifically includes:

performing parameter estimation by using a piecewise linear method, taking troposphere zenith delay as an unknown parameter, and using a step length of K delta_tThe discrete random process of (a) represents the change of tropospheric delay over time as follows:

and assuming that the tropospheric refraction in the zenith direction of the survey station varies linearly with time between epochs I to I + K as follows:

k is selected to reduce the number of refraction parameters rho (I) and rho (I + K) of the troposphere in the zenith direction of the observation station to be solved, and then a least square method is used for estimating the parameters;

and estimating parameters according to the time interval of N hours according to the time variation relation of the troposphere delay with time and the linear time variation relation of the troposphere refraction, wherein N is a positive integer greater than 0.

In the embodiment of the invention, the result after parameter estimation is input to a troposphere mapping function for correction; the troposphere mapping function is included in the GMF, NMF and VMF1 functions, including:

selecting a troposphere mapping function according to the relationship between the troposphere delay STD and the troposphere zenith delay ZTD of the observation station, wherein the relationship conforms to the formula: STD-m × ZTD, where m is a mapping function;

the NMF includes a dry component projection function m_dAnd a moisture component projection function m_w(ii) a The dry component projection function m_dThe expression of (a) is:

wherein E is a height angle; a is_ht＝2.53×10^-5；b_ht＝5.49×10^-3；c_ht＝1.14×10^-3(ii) a H is positive high, where the coefficient a_d、b_d、c_dAt a survey station latitude of between 15 ° and 75 °, the value is obtained by interpolation using the following formula:

in the formula, p represents a coefficient a to be interpolated_d、b_d、c_d(ii) a t is the cumulative year; t is t₀28 is the annual date of the reference moment;

wherein a is_d、b_d、c_dThe calculation formula of the equal coefficient when the latitude of the measuring station is less than 15 degrees is as follows:

a_d、b_d、c_dthe calculation formula of the equal coefficient when the latitude of the measuring station is more than 75 degrees is as follows:

projection function m of the wet component_wThe expression of (a) is:

in the formula, the coefficient a_w、b_w、c_wAt the measuring station latitude between 15 degrees and 75 degrees, the latitude is obtained by interpolation according to the following formula:

wherein, the latitude at the survey station is less thanAt 15 deg., take the value p at 15 deg_avg(ii) a When the latitude of the measuring station is more than 75 degrees, the value p at 75 degrees is taken_avg。

In the embodiment of the invention, the dry and wet component projection functions of the VMF1 are different from the dry and wet component projection functions of the NMF in a_d、b_d、c_dAnd a_w、b_w、c_wThe values of (A) are different; a of the VMF1_dAnd a_wThe coefficients are provided by a grid graph generated from measured meteorological data: the warp difference is 2.5 degrees, the weft difference is 2 degrees and the time interval is 6 hours; b_dTaking 0.0029 as a constant; c. C_dThen the following equation is used to calculate:

in the formula, b_wAnd c_wTaking a constant value, wherein b_w＝0.00146，c_w＝0.04391，c₀、c₁₁、c₁₀Is a constant;

the dry and wet component projection functions of the GMF are different from the dry and wet component projection functions of the VMF1 in that the coefficient a_dAnd a_wLatitude and longitude of station expressed as DAY of year DAY

And elevation H, said coefficient a_dAnd a_wThe calculation method is the same, and the expression is as follows:

in the formula, a₀Is an average value; a is the amplitude. The calculation is obtained by expanding to 9 th order through a spherical harmonic function expression:

in the embodiment of the present invention, the formula of the normalized mean square error value is as follows:

in the formula, N is the number of the stations; y is_iBaseline side length for day i; y is a weighted average of the side lengths of the single-day solution baselines;

is the error in the unit weight;

the baseline repeatability formula is:

in the formula, n is the total number of observation periods of the same baseline; c_iA certain component or side length of a baseline for a period of time;for the time period i corresponds to C_iThe variance of the components; for each time interval C_mIs calculated as the weighted average of (a).

According to the self-adaptive troposphere delay correction method and system for GNSS data processing, whether troposphere delay estimation is added or not is comprehensively considered, or when troposphere delay estimation is added, different troposphere delay correction empirical models, troposphere mapping functions and selection of troposphere delay parameter estimation are adopted for different conditions, various precision indexes are analyzed, an optimal solution of data processing is obtained through self-adaptive screening, and the precision of a processing result is improved.

Referring to fig. 2-9, an embodiment of the invention further provides a data processing method and a processing result based on the medium-long baseline GNSS framework network and the short baseline GNSS framework network, and the specific steps are as follows:

(1) and synchronously observing data through a survey station in a field data acquisition project engineering area, and collecting data of the provincial and municipal continuous operation reference station. And when tropospheric delay is not estimated, relevant model correction is not carried out, and the step of optimal solution analysis is directly carried out. When estimating tropospheric delay, the satellite height angle is input and the next step is performed.

(2) And selecting a troposphere delay correction model (Hopfield, Saastamoinen, Black and the like) to correct the zenith direction, estimating troposphere zenith delay parameters by adopting a piecewise linearity method, and estimating one parameter by taking 1, 2, 3 and 4 … … hours sequentially along with the change of time.

(3) According to the relation between troposphere delay STD and troposphere zenith delay ZTD of the observation station: tropospheric mapping functions (GMF, NMF, VMF1, … …) are selected for tropospheric delay correction in the projection direction.

(4) And comprehensively evaluating and screening the data processing results of the steps by indexes such as Normalized Root Mean Square (NRMS), baseline repeatability, component precision of each baseline and the like. Through analysis and comparison of various indexes, the minimum qualified requirement is met, namely NRMS is smaller than 0.3, baseline repeatability and component precision of various baselines are compared, a better result is obtained, poor result items are removed, and then iterative loop processing is carried out again until an optimal tropospheric delay estimation data processing result is screened out.

(5) According to factors such as distance between stations, altitude difference, weather conditions and observation environment, a medium-long baseline GNSS frame network and a short baseline GNSS basic network are respectively used as test areas, and a self-adaptive troposphere delay correction method is applied to carry out data processing. The GNSS network in the test area is located in a low-altitude area of coastal areas in south China, belongs to subtropical marine monsoon climate, is high in temperature, wet and rainy all year round, has more typhoons and tropical cyclones in summer, is mainly characterized by river valley impact plains and few hills in landform, and has the average distance of about 33 kilometers between measurement stations of a medium-long baseline GNSS frame network (as shown in figure 2). The point-to-point distance between the survey stations of the short-baseline GNSS basic network is 11 kilometers (as shown in figure 3), the acquisition time of observation data is 9-10 months, and the weather conditions are mostly sunny days or cloudy days.

(6) After data processing is carried out, and an unqualified data processing result with the NRMS value larger than 0.3 is removed, an optimal solution of data processing is obtained, wherein table 1 is base line repeatability statistics of a medium-long base line GNSS framework network, and table 2 is base line statistics of a short base line GNSS basic network. Table 3 is medium and long baseline GNSS framework network baseline accuracy statistics, and table 4 is short baseline GNSS base network baseline accuracy statistics:

TABLE 1

TABLE 2

TABLE 3

TABLE 4

As can be seen from tables 1 and 2, when the medium-long baseline GNSS framework network data is processed, the baseline repeatability and the variance are good and small by using the conventional troposphere delay correction method and the adaptive troposphere delay correction method. When the short-baseline GNSS basic network data is processed, the baseline repeatability of the two methods is good, and the baseline repeatability of the adaptive troposphere delay correction method is slightly superior to that of the conventional troposphere delay correction method.

As can be seen from table 3, when the medium-long baseline GNSS framework network data is processed, the adaptive tropospheric delay correction method is better than the conventional tropospheric delay correction method in the N, E, U direction. Wherein the maximum values in the N, E, U directions are respectively 4.5mm, 5.0mm and 20.9mm, the precision is respectively improved by 6.25%, 7.41% and 9.91%, the minimum values are respectively 2.2mm, 2.5mm and 9.8mm, the precision is respectively improved by 15.38%, 16.67% and 24.03%, the average values are respectively 3.2mm, 3.6mm and 14.2mm, and the precision is respectively improved by 14.96%, 14.40% and 21.42%. As can be seen from fig. 4-6, the baseline accuracy of each baseline sequence is improved as a whole after the adaptive tropospheric delay correction method is applied in the direction N, E, U.

As can be seen from table 4, when data processing is performed on the short-baseline GNSS basic network, the conventional troposphere delay correction method and the adaptive troposphere delay correction method are respectively adopted, the baseline accuracy maximum value, minimum value and average value in the N, E direction do not change greatly, and the difference change is mainly reflected in the U direction, wherein the maximum value is 14.4mm, the minimum value is 5.5mm, and the average value is 7.7mm by adopting the conventional troposphere delay correction method; the maximum value of the adaptive troposphere delay correction method is 6.3mm, the precision is improved by 66.31%, the minimum value is 1.8mm, the precision is improved by 65.38%, the average value is 2.9mm, and the precision is improved by 66.00%. As can be seen from fig. 7 to 9, the difference of the baseline sequences in the direction N, E is not large, and the baseline accuracy after the adaptive tropospheric delay correction method is adopted in the direction U is improved as a whole.

The method has the advantages that the method is applied to urban geodetic reference construction in complex geographic environments in coastal areas of south China, compared with a conventional troposphere delay correction method, a high-precision GNSS baseline data processing result is obtained by adopting a self-adaptive troposphere delay correction method, the baseline average precision of a medium-long baseline GNSS frame network in the N, E, U direction is respectively improved by 14.96%, 14.40% and 21.42%, the baseline average precision of a short baseline GNSS basic network in the U direction is improved by 66.00%, and the baseline repeatability precision is higher. Meanwhile, the complex process of manual intervention and repeated trial calculation on the troposphere delay correction method is solved, the data processing efficiency of the GNSS is improved through data processing iteration circulation of the self-adaptive troposphere delay correction method and analysis and screening of the optimal solution, and important data processing guarantee is provided for establishing a high-precision GNSS three-dimensional geodetic reference.

Referring to fig. 10, an embodiment of the invention further provides a system for adaptive tropospheric delay correction for GNSS data processing, including:

the data judgment unit is used for acquiring synchronous observation data of a survey station in a project area and judging whether troposphere delay estimation is carried out or not according to the synchronous observation data; if not, taking the synchronous observation data as a first data processing result; if so, repeating the steps executed by the loop processing unit until the optimal result of tropospheric delay estimation is screened out to be used as a second data processing result;

the circulation processing unit is used for inputting the result after the estimation of the troposphere delay into the troposphere delay correction model for operation; the models include a hopfield model, a Sasta morningine model and a Brookfield model; performing parameter estimation on the operated result by a piecewise linearity method and a least square method; inputting the result after parameter estimation into a troposphere mapping function for correction; the troposphere mapping function is included in the GMF, NMF and VMF1 functions; sequentially processing the corrected result by a standardized mean square deviation value, baseline repeatability and each baseline component precision, screening out a result of which the standardized mean square deviation value is less than 0.3, comparing the baseline repeatability of the data with each baseline component precision, and taking a better result;

and the optimal result acquisition unit is used for performing optimal solution analysis on the first data processing result and the second data processing result according to the distance, the altitude difference, the climate condition and the observation environmental factors among the engineering area measuring stations to obtain the optimal result of the troposphere delay data processing.

An embodiment of the present invention further provides a computer terminal device, including:

one or more processors;

a memory coupled to the processor for storing one or more programs;

when executed by the one or more processors, cause the one or more processors to implement an adaptive tropospheric delay correction method for GNSS data processing as described in any of the embodiments above.

In this embodiment, the processor is configured to control the overall operation of the computer terminal device, so as to complete all or part of the steps of the above-mentioned full-automatic power consumption prediction method. The memory is used to store various types of data to support the operation at the computer terminal device, which data may include, for example, instructions for any application or method operating on the computer terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.

The computer terminal Device may be implemented by one or more application specific integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to perform the above adaptive tropospheric delay correction method for GNSS data Processing, and achieve technical effects consistent with the above method.

An embodiment of the invention also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, implements an adaptive tropospheric delay correction method for GNSS data processing as described in any of the above embodiments.

While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.