CN111708050A - Adaptive troposphere delay correction method and system for GNSS data processing - Google Patents

Adaptive troposphere delay correction method and system for GNSS data processing Download PDF

Info

Publication number
CN111708050A
CN111708050A CN202010482862.8A CN202010482862A CN111708050A CN 111708050 A CN111708050 A CN 111708050A CN 202010482862 A CN202010482862 A CN 202010482862A CN 111708050 A CN111708050 A CN 111708050A
Authority
CN
China
Prior art keywords
data processing
troposphere
result
baseline
adaptive
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010482862.8A
Other languages
Chinese (zh)
Other versions
CN111708050B (en
Inventor
刘小丁
周建营
陈国恒
张惠军
朱紫阳
董斌斌
张文峰
华水胜
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
SURVEYING AND MAPPING INSTITUTE LANDS AND RESOURCE DEPARTMENT OF GUANGDONG PROVINCE
Original Assignee
SURVEYING AND MAPPING INSTITUTE LANDS AND RESOURCE DEPARTMENT OF GUANGDONG PROVINCE
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by SURVEYING AND MAPPING INSTITUTE LANDS AND RESOURCE DEPARTMENT OF GUANGDONG PROVINCE filed Critical SURVEYING AND MAPPING INSTITUTE LANDS AND RESOURCE DEPARTMENT OF GUANGDONG PROVINCE
Priority to CN202010482862.8A priority Critical patent/CN111708050B/en
Publication of CN111708050A publication Critical patent/CN111708050A/en
Application granted granted Critical
Publication of CN111708050B publication Critical patent/CN111708050B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/03—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers
    • G01S19/07—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing data for correcting measured positioning data, e.g. DGPS [differential GPS] or ionosphere corrections
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/40—Correcting position, velocity or attitude
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/15—Correlation function computation including computation of convolution operations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

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.

Description

Adaptive troposphere delay correction method and system for GNSS data processing
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.
In the prior art, when tropospheric delay is studied, research is often performed from a certain influence factor, and only one method is adopted for error correction. In fact, because GNSS networks in different areas have differences of many different factors, it is simple to directly introduce any tropospheric delay correction strategy, which has certain limitations and irreproducibility. Meanwhile, measurement personnel often perform repeated trial calculation due to manual intervention in the data processing process, and the optimal solution of the data is often difficult to ensure; or the accuracy of the data processing result is affected by improper processing of a certain detail.
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:
Figure BDA0002516932510000021
wherein, Δ Sd、ΔSwM 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:
Figure BDA0002516932510000031
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),
Figure BDA0002516932510000032
wherein
Figure BDA0002516932510000033
The latitude of the survey station; h issElevation of the survey station (in km); b is hsA list function of; r is E and hsA list function of; pS、TS、esIs a meteorological element on the survey station; the above formula can be expressed as after numerical fitting：
Figure BDA0002516932510000034
The blanc model formula is:
Figure BDA0002516932510000035
wherein the elevation angle E is in degrees and R issFor measuring the distance from the station to the center of the earth,/0And b (E) as path correction parameters, dhAnd dwRespectively, 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 deltatThe discrete random process of (a) represents the change of tropospheric delay over time as follows:
Figure BDA0002516932510000041
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:
Figure BDA0002516932510000042
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 mdAnd a moisture component projection function mw(ii) a The dry component projection function mdThe expression of (a) is:
Figure BDA0002516932510000043
Figure BDA0002516932510000044
wherein E is a height angle; a isht＝2.53×10-5；bht＝5.49×10-3；cht＝1.14×10-3(ii) a H is positive high, where the coefficient ad、bd、cdAt a survey station latitude of between 15 ° and 75 °, the value is obtained by interpolation using the following formula:
Figure BDA0002516932510000045
Figure BDA0002516932510000051
in the formula, p represents a coefficient a to be interpolatedd、bd、cd(ii) a t is the cumulative year; t is t028 is the annual date of the reference moment;
wherein a isd、bd、cdThe calculation formula of the equal coefficient when the latitude of the measuring station is less than 15 degrees is as follows:
Figure BDA0002516932510000052
ad、bd、cdthe calculation formula of the equal coefficient when the latitude of the measuring station is more than 75 degrees is as follows:
Figure BDA0002516932510000053
projection function m of the wet componentwThe expression of (a) is:
Figure BDA0002516932510000054
in the formula, the coefficient aw、bw、cwAt the measuring station latitude between 15 degrees and 75 degrees, the latitude is obtained by interpolation according to the following formula:
Figure BDA0002516932510000055
wherein, when the latitude of the measuring station is less than 15 degrees, the value p at 15 degrees is takenavg(ii) a When the latitude of the measuring station is more than 75 degrees, the value p at 75 degrees is takenavg。
Preferably, the dry and wet component projection functions of VMF1 are different from the dry and wet component projection functions of NMF in ad、bd、cdAnd aw、bw、cwThe values of (A) are different; a of the VMF1dAnd awThe 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; bdTaking 0.0029 as a constant; c. CdThen the following equation is used to calculate:
Figure BDA0002516932510000056
in the formula, bwAnd cwTaking a constant value, wherein bw＝0.00146，cw＝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 adAnd awLatitude and longitude of station expressed as DAY of year DAY
Figure BDA0002516932510000061
And elevation H, said coefficient adAnd awThe calculation method is the same, and the expression is as follows:
Figure BDA0002516932510000062
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:
Figure BDA0002516932510000063
preferably, the formula of the normalized mean square error value is:
Figure BDA0002516932510000064
in the formula, N is the number of the stations; y isiBaseline side length for day i; y is a weighted average of the side lengths of the single-day solution baselines;
Figure BDA0002516932510000065
is the error in the unit weight;
the baseline repeatability formula is:
Figure BDA0002516932510000066
in the formula, n is the total number of observation periods of the same baseline; ciA certain component or side length of a baseline for a period of time;
Figure BDA0002516932510000067
for the time period i corresponds to CiThe variance of the components; for each time interval CmIs 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:
Figure BDA0002516932510000111
wherein, Δ Sd、ΔSwM 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:
Figure BDA0002516932510000121
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),
Figure BDA0002516932510000122
wherein
Figure BDA0002516932510000123
The latitude of the survey station; h issElevation of the survey station (in km); b is hsA list function of; r is E and hsA list function of; pS、TS、esIs a meteorological element on the survey station; the above formula can be expressed by numerical fitting:
Figure BDA0002516932510000124
the blanc model formula is:
Figure BDA0002516932510000125
wherein the elevation angle E is in degrees and R issFor measuring the distance from the station to the center of the earth,/0And b (E) as path correction parameters, dhAnd dwRespectively, 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 deltatThe discrete random process of (a) represents the change of tropospheric delay over time as follows:
Figure BDA0002516932510000126
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:
Figure BDA0002516932510000131
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 mdAnd a moisture component projection function mw(ii) a The dry component projection function mdThe expression of (a) is:
Figure BDA0002516932510000132
Figure BDA0002516932510000133
wherein E is a height angle; a isht＝2.53×10-5；bht＝5.49×10-3；cht＝1.14×10-3(ii) a H is positive high, where the coefficient ad、bd、cdAt a survey station latitude of between 15 ° and 75 °, the value is obtained by interpolation using the following formula:
Figure BDA0002516932510000134
in the formula, p represents a coefficient a to be interpolatedd、bd、cd(ii) a t is the cumulative year; t is t028 is the annual date of the reference moment;
wherein a isd、bd、cdThe calculation formula of the equal coefficient when the latitude of the measuring station is less than 15 degrees is as follows:
Figure BDA0002516932510000141
ad、bd、cdthe calculation formula of the equal coefficient when the latitude of the measuring station is more than 75 degrees is as follows:
Figure BDA0002516932510000142
projection function m of the wet componentwThe expression of (a) is:
Figure BDA0002516932510000143
in the formula, the coefficient aw、bw、cwAt the measuring station latitude between 15 degrees and 75 degrees, the latitude is obtained by interpolation according to the following formula:
Figure BDA0002516932510000144
wherein, the latitude at the survey station is less thanAt 15 deg., take the value p at 15 degavg(ii) a When the latitude of the measuring station is more than 75 degrees, the value p at 75 degrees is takenavg。
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 ad、bd、cdAnd aw、bw、cwThe values of (A) are different; a of the VMF1dAnd awThe 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; bdTaking 0.0029 as a constant; c. CdThen the following equation is used to calculate:
Figure BDA0002516932510000145
in the formula, bwAnd cwTaking a constant value, wherein bw＝0.00146，cw＝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 adAnd awLatitude and longitude of station expressed as DAY of year DAY
Figure BDA0002516932510000146
And elevation H, said coefficient adAnd awThe calculation method is the same, and the expression is as follows:
Figure BDA0002516932510000151
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:
Figure BDA0002516932510000152
in the embodiment of the present invention, the formula of the normalized mean square error value is as follows:
Figure BDA0002516932510000153
in the formula, N is the number of the stations; y isiBaseline side length for day i; y is a weighted average of the side lengths of the single-day solution baselines;
Figure BDA0002516932510000154
is the error in the unit weight;
the baseline repeatability formula is:
Figure BDA0002516932510000155
in the formula, n is the total number of observation periods of the same baseline; ciA certain component or side length of a baseline for a period of time;
Figure BDA0002516932510000156
for the time period i corresponds to CiThe variance of the components; for each time interval CmIs 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:
Figure BDA0002516932510000161
TABLE 1
Figure BDA0002516932510000162
TABLE 2
Figure BDA0002516932510000171
TABLE 3
Figure BDA0002516932510000172
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.

Claims (10)

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:
Figure FDA0002516932500000011
wherein, Δ Sd、ΔSwM 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:
Figure FDA0002516932500000021
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),
Figure FDA0002516932500000022
wherein
Figure FDA0002516932500000023
The latitude of the survey station; h issElevation of the survey station (in km); b is hsA list function of; r is E and hsA list function of; pS、TS、esIs a meteorological element on the survey station; the above formula can be expressed by numerical fitting:
Figure FDA0002516932500000024
the blanc model formula is:
Figure FDA0002516932500000025
wherein the elevation angle E is in degrees and R issFor measuring the distance from the station to the center of the earth,/0And b (E) as path correction parameters, dhAnd dwRespectively, 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 deltatThe discrete random process of (a) represents the change of tropospheric delay over time as follows:
Figure FDA0002516932500000031
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:
Figure FDA0002516932500000032
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 mdAnd a moisture component projection function mw(ii) a The dry component projection function mdThe expression of (a) is:
Figure FDA0002516932500000033
wherein E is a height angle; a isht＝2.53×10-5；bht＝5.49×10-3；cht＝1.14×10-3(ii) a H is positive high, where the coefficient ad、bd、cdAt a survey station latitude of between 15 ° and 75 °, the value is obtained by interpolation using the following formula:
Figure FDA0002516932500000041
in the formula, p represents a coefficient a to be interpolatedd、bd、cd(ii) a t is the cumulative year; t is t028 is the annual date of the reference moment;
wherein a isd、bd、cdThe calculation formula of the equal coefficient when the latitude of the measuring station is less than 15 degrees is as follows:
Figure FDA0002516932500000042
ad、bd、cdthe calculation formula of the equal coefficient when the latitude of the measuring station is more than 75 degrees is as follows:
Figure FDA0002516932500000043
projection function m of the wet componentwThe expression of (a) is:
Figure FDA0002516932500000044
in the formula, the coefficient aw、bw、cwAt the measuring station latitude between 15 degrees and 75 degrees, the latitude is obtained by interpolation according to the following formula:
Figure FDA0002516932500000045
wherein, when the latitude of the measuring station is less than 15 degrees, the value p at 15 degrees is takenavg(ii) a When the latitude of the measuring station is more than 75 degrees, the value p at 75 degrees is takenavg。
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 ad、bd、cdAnd aw、bw、cwThe values of (A) are different; a of the VMF1dAnd awThe 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; bdTaking 0.0029 as a constant; c. CdThen the following equation is used to calculate:
Figure FDA0002516932500000051
in the formula, bwAnd cwTaking a constant value, wherein bw＝0.00146，cw＝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 adAnd awLatitude and longitude of station expressed as DAY of year DAY
Figure FDA0002516932500000052
And elevation H, said coefficient adAnd awThe calculation method is the same, and the expression is as follows:
Figure FDA0002516932500000053
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:
Figure FDA0002516932500000054
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:
Figure FDA0002516932500000055
in the formula, N is the number of the stations; y isiBaseline side length for day i; y is a weighted average of the side lengths of the single-day solution baselines;
Figure FDA0002516932500000056
is the error in the unit weight;
the baseline repeatability formula is:
Figure FDA0002516932500000057
in the formula, n is the total number of observation periods of the same baseline; ciA certain component or side length of a baseline for a period of time;
Figure FDA0002516932500000058
for the time period i corresponds to CiThe variance of the components; for each time interval CmIs calculated as the weighted average of (a).
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.
CN202010482862.8A 2020-05-29 2020-05-29 Adaptive troposphere delay correction method and system for GNSS data processing Active CN111708050B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010482862.8A CN111708050B (en) 2020-05-29 2020-05-29 Adaptive troposphere delay correction method and system for GNSS data processing

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010482862.8A CN111708050B (en) 2020-05-29 2020-05-29 Adaptive troposphere delay correction method and system for GNSS data processing

Publications (2)

Publication Number Publication Date
CN111708050A true CN111708050A (en) 2020-09-25
CN111708050B CN111708050B (en) 2023-04-07

Family

ID=72539032

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010482862.8A Active CN111708050B (en) 2020-05-29 2020-05-29 Adaptive troposphere delay correction method and system for GNSS data processing

Country Status (1)

Country Link
CN (1) CN111708050B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112130173A (en) * 2020-09-29 2020-12-25 长安大学 Atmospheric correction method for GNSS reflection elevation measurement
CN112783981A (en) * 2021-02-05 2021-05-11 腾讯科技（深圳）有限公司 Method and device for processing and monitoring data in reference station network
CN116361714A (en) * 2023-06-01 2023-06-30 山东科技大学 Non-isotropic horizontal troposphere delay classification method

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103323888A (en) * 2013-04-24 2013-09-25 东南大学 Method for eliminating delay errors of troposphere of GNSS atmospheric probing data
CN103744091A (en) * 2013-11-15 2014-04-23 福建纳威导航科技有限责任公司 Technology for reducing satellite observation cut-off height angle in precision positioning
CN105629263A (en) * 2015-12-21 2016-06-01 广州中海达卫星导航技术股份有限公司 Troposphere atmosphere delay error correction method and correction system
US20170045624A1 (en) * 2015-08-14 2017-02-16 Trimble Navigation Limited Navigation satellite system positioning involving the generation of advanced correction information

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103323888A (en) * 2013-04-24 2013-09-25 东南大学 Method for eliminating delay errors of troposphere of GNSS atmospheric probing data
CN103744091A (en) * 2013-11-15 2014-04-23 福建纳威导航科技有限责任公司 Technology for reducing satellite observation cut-off height angle in precision positioning
US20170045624A1 (en) * 2015-08-14 2017-02-16 Trimble Navigation Limited Navigation satellite system positioning involving the generation of advanced correction information
CN105629263A (en) * 2015-12-21 2016-06-01 广州中海达卫星导航技术股份有限公司 Troposphere atmosphere delay error correction method and correction system

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112130173A (en) * 2020-09-29 2020-12-25 长安大学 Atmospheric correction method for GNSS reflection elevation measurement
CN112783981A (en) * 2021-02-05 2021-05-11 腾讯科技（深圳）有限公司 Method and device for processing and monitoring data in reference station network
CN112783981B (en) * 2021-02-05 2022-04-08 腾讯科技（深圳）有限公司 Method and device for processing and monitoring data in reference station network
CN116361714A (en) * 2023-06-01 2023-06-30 山东科技大学 Non-isotropic horizontal troposphere delay classification method
CN116361714B (en) * 2023-06-01 2023-08-04 山东科技大学 Non-isotropic horizontal troposphere delay classification method

Also Published As

Publication number Publication date
CN111708050B (en) 2023-04-07

Similar Documents

Publication Publication Date Title
CN111708050B (en) Adaptive troposphere delay correction method and system for GNSS data processing
Gharbia et al. Spatially distributed potential evapotranspiration modeling and climate projections
Stathopoulos et al. Wind power prediction based on numerical and statistical models
CN111461909B (en) Short-term prediction method for farmland evapotranspiration
CN107316095B (en) Regional weather drought level prediction method coupled with multi-source data
Li et al. Assessment of the global monthly mean surface insolation estimated from satellite measurements using global energy balance archive data
KR101856042B1 (en) Bias correction method of extreme precipitation data in global climate model using mixture distributions
CN114019579B (en) High space-time resolution near-surface air temperature reconstruction method, system and equipment
CN110501761B (en) Forecasting method for predicting and forecasting crops ETc in different forecast periods
Moreido et al. Assessing possible changes in Selenga R. water regime in the XXI century based on a runoff formation model
CN110031877A (en) A kind of region NWP tropospheric delay correction method based on GRNN model
CN113536576B (en) Method and system for correcting distance integral power statistics of numerical forecast product
CN115345076A (en) Wind speed correction processing method and device
CN108154193A (en) A kind of long-term sequence precipitation data NO emissions reduction method
CN110210142B (en) Real-time water demand measuring and calculating method for rice in large irrigation areas in south
CN113639893B (en) Near-earth weighted average temperature information acquisition method based on multiple meteorological factors
CN109241212B (en) Retrieval method for historical rainfall based on mesoscale numerical atmospheric mode and high resolution
CN106355264B (en) Reference crop evapotranspiration combined prediction method
CN114970222B (en) HASM-based regional climate mode daily average air temperature deviation correction method and system
Tait et al. Interpolation of daily solar radiation for New Zealand using a satellite data-derived cloud cover surface
CN114707101A (en) Evapotranspiration estimation method and device and electronic equipment
Khain et al. CALMO-Progress Report
CN109886551A (en) The method that the placement error and quantization error generated by big data to farm environment monitoring modular device is corrected
CN117436710B (en) Drought assessment method based on crop model
CN111950156B (en) Snow water equivalent determining method, device, computer equipment and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant