CN117454679B - Dynamic linear ionosphere modeling method and system based on linear distribution measuring station - Google Patents
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Abstract
The invention relates to a dynamic linear ionosphere modeling method and system based on linear distribution measuring stations. The existing ionosphere modeling method is insufficient in precision when processing linear areas with large spans and complex relief. The method selects adjacent CORS stations which are linearly distributed at two sides of a point to be detected as observation points; acquiring an initial value of TEC data to be measured; constructing linear ionosphere models of all orders according to the number of observation points; carrying out residual estimation dynamic order determination on each order of linear ionosphere model, and determining the belonging order of the optimal linear ionosphere model; and carrying out iterative updating on TEC data to be measured according to the belonging order of the optimal linear ionosphere model. According to the invention, polynomial fitting modeling is carried out by using CORS site observation values distributed in a scribbled mode at the same time to obtain a plurality of ionosphere models containing high-order changes, the model order is dynamically ordered by using the data of the last epoch, so that the self-adaptive optimization of the model is realized, and the accuracy of the model in an extreme weather environment is ensured.
Description
Technical Field
The invention relates to the technical field of ionosphere monitoring and modeling, in particular to a dynamic linear ionosphere modeling method and system based on linear distribution measuring stations.
Background
The ionosphere is a partially ionized atmosphere region, is an important component of radio communication, and the knowledge of the structural principle of the ionosphere is helpful for the development of satellite navigation, communication, time service and other technologies to higher precision.
The ionosphere modeling methods commonly used at present can be broadly divided into the following categories: ① Global modeling using spherical harmonics, including CODE (Center for Orbit Determination in Europe, european orbital center), ESA (European SPACE AGENCY ), and WHU (Wuhan University, university of martial arts); ② Modeling using spline curve fitting, including JPL (Jet Propulsion Laboratory, U.S. jet propulsion laboratories); ③ Modeling using generalized trigonometric series functions, including CAS (CHINESE ACADEMY of Sciences, china academy of Sciences); ④ Modeling was performed using a level-by-level tomographic interpolation scheme, including UPC (Universitat Polit re cnica de Catalunya, university of Spanish, galois, inc.). The ionosphere modeling methods are all ionosphere modeling methods adopted by IGS (International GNSS SERVICE, international GNSS service organization) data centers of all countries of the world, and ionosphere TEC (Total Electron Content, total electronic content of the ionosphere) products for representing the change of ionosphere delay values can be provided in a global or regional grid, but for linear areas with large spans, complex topography fluctuation of traffic route systems, industries along the lines and the like, the traditional ionosphere modeling methods are difficult to provide ionosphere TEC products with enough precision.
Therefore, a new ionosphere modeling method is necessary to be proposed to meet the requirements of linear regions with large region spans and complex relief.
Disclosure of Invention
The invention aims to provide a dynamic linear ionosphere modeling method and system based on linear distribution measuring stations, which are used for solving the problem of insufficient precision existing in the conventional ionosphere modeling method when linear areas with large span and complex topography fluctuation are processed.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
A method for modeling a dynamic linear ionosphere based on linear distribution stations, the method comprising:
Selecting a plurality of adjacent CORS stations which are linearly distributed on two sides of a point to be detected as observation points;
Acquiring an initial value of TEC data to be measured;
constructing linear ionosphere models of all orders according to the number of observation points;
Carrying out residual estimation dynamic order determination on each order of linear ionosphere model, and determining the belonging order of the optimal linear ionosphere model;
And carrying out iterative updating on TEC data to be measured according to the belonging order of the optimal linear ionosphere model.
Further, obtaining an initial value of TEC data of the to-be-measured point includes:
Obtaining an approximate value of TEC data of a to-be-measured point;
Obtaining TEC data of an observation point of the next epoch;
and taking the approximation value of the TEC data of the to-be-measured point and the TEC data of the observation point of the next epoch as initial values of the TEC data of the to-be-measured point, namely initial input parameters of the model.
Further, according to the number of observation points, constructing a linear ionosphere model of each order of corresponding order, including:
the TEC data of the ionosphere is regarded as a function of variables such as geographic position, time and the like;
At the same time, carrying out Taylor series expansion on the function to obtain a function Taylor series expansion;
when the number of observation points is n, the corresponding order is n/2, and the linear ionosphere model of each order is obtained.
Further, regarding the TEC data of the ionosphere as a function of variables such as geographic location and time, including:
；
wherein:
TEC data for ionosphere;
is a position vector;
Is a time variable.
Further, at the same time, performing taylor series expansion on the function to obtain a taylor series expansion of the function, including:
；
wherein:
the latitude or longitude of the point to be measured;
I=1, 2, …, n, i is the latitude or longitude of the i-th observation point TEC data;
i is also the number of taylor series expansion.
Further, obtaining a linear ionosphere model of each step, including:
；
wherein:
Errors are fitted to the model.
Further, performing residual estimation dynamic order determination on each order of linear ionosphere model to determine the belonging order of the optimal linear ionosphere model, including:
inputting initial values of TEC data of to-be-measured points into each step of linear ionosphere model;
Calculating model residuals corresponding to the to-be-measured point and the observation point under the linear ionosphere model of each order, and obtaining weighted average residuals after weighted average ；
For a pair ofComparison, when/>And when i is determined to be the affiliated order of the optimal linear ionosphere model at the current moment T 0, the dynamic order determination of residual estimation is completed.
Further, a weighted average residual error is obtained after weighted averageComprising:
；
wherein:
A weighted average residual error which is the residual value of the linear ionosphere model of each order;
The weight value is the weight value of the point to be measured;
The weight of the observation point;
residual errors of the to-be-measured points under the linear ionosphere model of each order are obtained;
The residual error of the observation point under the linear ionosphere model of each order is obtained.
Further, according to the order of the optimal linear ionosphere model, iteratively updating TEC data to be measured, including:
calculating TEC data of a to-be-measured point according to the belonging order of the optimal linear ionosphere model at the current moment T 0;
Taking TEC data to be measured as linear ionosphere model input parameters of the next epoch T 0 +1;
repeating the steps to realize iterative updating of TEC data of the to-be-measured point.
In another aspect, a dynamic linear ionosphere modeling system based on linear distribution stations is provided, the system for implementing the method, comprising:
the selecting module is used for selecting a plurality of adjacent CORS stations which are linearly distributed on two sides of the point to be detected as observation points;
the acquisition module is used for acquiring an initial value of TEC data to be measured;
the construction module is used for constructing linear ionosphere models of all orders corresponding to the number of observation points;
The determining module is used for carrying out residual estimation dynamic order determination on each order of linear ionosphere model and determining the number of the order of the optimal linear ionosphere model;
and the updating module is used for carrying out iterative updating on the TEC data to be measured point according to the belonging order of the optimal linear ionosphere model.
Compared with the prior art, the invention has the following beneficial effects:
The invention provides a dynamic linear ionosphere modeling method and a system based on linear distribution measuring stations, aiming at the geographic distribution characteristics of an aerial ionosphere in the longitudinal direction of a linear region, polynomial fitting modeling is carried out by using the observed values of CORS (Continuously Operating Reference Stations ) stations which are distributed in a line shape at the same time to obtain a plurality of ionosphere electron content sequence models containing high-order changes, meanwhile, the model order of the linear ionosphere is dynamically ordered by utilizing the data of the last epoch, the self-adaptive optimization of the model is realized, the precision of an ionosphere model product in an extreme weather environment is ensured, and the problems that the traditional ionosphere modeling method is difficult to be applied to the linear region with large span and complex relief in the region such as a traffic route system, the line industry and the like, and the high-precision positioning, speed measurement and time service quality is poor are solved. In addition, because the ionosphere model can select stations required by modeling according to ionosphere distribution characteristics, the station spacing can be properly enlarged, the number of stations can be reduced in the actual station building application process, and the cost of manpower and material resources for building the stations and subsequent maintenance is greatly reduced.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a schematic view of fitting of a plurality of observation points.
FIG. 3 is a schematic diagram of the accuracy of the linear ionosphere model in the low and medium latitude areas of China.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. The drawings illustrate preferred embodiments of the invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It should be noted that like reference numerals and letters refer to like items, and thus once an item is defined in one embodiment, no further definition or explanation thereof is necessary in subsequent embodiments. Furthermore, the terms "comprises," "comprising," and the like, as well as any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus.
It should also be noted that although the order of steps is referred to in the method description, in some cases it may be performed in a different order than here, and should not be construed as limiting the order of steps.
Example 1:
The embodiment provides a dynamic linear ionosphere modeling method based on linear distribution measuring stations, which aims at the characteristic that the ionosphere has stronger change regularity in each linear direction, performs polynomial fitting modeling by using the observed values of CORS stations with simultaneous linear distribution to obtain an ionosphere electron content sequence model containing high-order change, namely a linear ionosphere model of each order, and dynamically determining the order of the model by combining the data of the last epoch, so that the self-adaptive optimization of the model is realized, and the product precision of the ionosphere model in extreme weather environment is ensured.
The method comprises the following steps:
s1: and selecting a plurality of adjacent CORS stations which are linearly distributed on two sides of the point to be measured as observation points. Specifically, CORS stations of a first near, a second near and a third near … are sequentially selected from two sides of a point to be measured as observation points, and the observation points are sequentially connected and distributed in a linear shape.
S2: obtaining an initial value of TEC data of a to-be-measured point comprises the following steps:
S201: and obtaining an approximate value of TEC data to be measured by a carrier phase pseudo-range smoothing method or a PPP (precise point position, precise single point positioning) non-difference non-combination estimation method and the like.
S202: obtaining TEC data of an observation point of the next epoch;
S203: and taking the approximation value of the TEC data of the to-be-measured point and the TEC data of the observation point of the next epoch as initial values of the TEC data of the to-be-measured point, namely initial input parameters of the model.
S3: according to the number of observation points, constructing each order linear ionosphere model with corresponding orders, which comprises the following steps:
s301: because the ionized layer has strong change regularity in each linear direction, the TEC data of the ionized layer can be regarded as a function of variables such as geographic position, time and the like:
；
wherein:
TEC data for ionosphere;
is a position vector;
Is a time variable.
The matrix comprises longitude and latitude of the to-be-measured point and the observation point.
S302: at the same time, carrying out Taylor series expansion on the function to obtain a function Taylor series expansion:
；
wherein:
the latitude or longitude of the to-be-measured point depends on the direction of the modeling area;
I=1, 2, …, n, i is the latitude or longitude of the i-th observation point TEC data;
i is also the number of taylor series expansion.
S303: when the number of observation points is n, the corresponding order is n/2, and the linear ionosphere model of each order is obtained:
；
wherein:
Errors are fitted to the model.
Taking the example of selecting the CORS sites on the left side and the right side of the to-be-measured point as the observation point, further explaining S3:
because the linear ionosphere model selects a point with consistent distance from the left side to the right side of the point to be measured when the observation point is added, 2 observation points are added each time, and the corresponding model order is +1. Selecting CORS sites on the left side and the right side of the to-be-measured point, wherein the number of CORS sites adjacent to the to-be-measured point is 2, and the Taylor series expansion is only carried out to a first order item, so that the function Taylor series expansion of the CORS sites on the left side and the right side of the to-be-measured point is obtained:
；
wherein:
The coordinates of the observed value at the left side of the point to be measured;
the coordinates of the observed value on the right side of the point to be measured;
Residual errors after Taylor series expansion of the CORS site on the left side;
the residual after Taylor series expansion is the CORS site on the right side.
Adding the function Taylor series expansion of the CORS sites at the left side and the right side of the to-be-measured point to obtain a relational expression:
；
namely a first order linear ionosphere model.
Wherein:
And (5) taking the sum of residual errors after Taylor series expansion of CORS stations at two sides of the point to be measured as a model fitting error.
The right side of the above formula can be divided mainly into two parts: one part is as followsObservations representing the CORS sites on the left and right are part of the TEC data for the point to be measured. Another part is/>The interpolation result of the TEC data of the to-be-measured point is influenced by two variables in the model, one is the altitude difference between the to-be-measured point and the observation point, the other is the change rate of the TEC data of the observation point, and the interpolation result is the weighted average of the distances between the to-be-measured point and the observation results of the two observation points on the basis of the assumption that the change gradients at the two points are consistent under the condition that only two points are used for fitting a straight line:
；
The model fitting error is mainly caused by a first-order linear ionosphere model, and when the site spacing is fixed, the smaller the position curve gradient between the observation point and the to-be-measured point is, the closer the model interpolation result is to the true value.
Referring to fig. 2, by introducing more surrounding site information, the curvature change of the ionosphere can be estimated, and a higher-order ionosphere change rate can be obtained corresponding to the above equation, so that the interpolation effect can be effectively improved by increasing the number of reference points. Meanwhile, as the high-step change of the ionosphere is obtained, the ionosphere change condition with higher precision in the range can be obtained by using a few stations, and the dependence of ionosphere modeling on the station spacing is effectively reduced.
S4: carrying out residual estimation dynamic order determination on each order of linear ionosphere model, and determining the belonging order of the optimal linear ionosphere model, wherein the method comprises the following steps:
s401: and inputting initial values of TEC data of the to-be-measured points into each step of linear ionosphere model.
S402: calculating model residuals corresponding to the to-be-measured point and the observation point under the linear ionosphere model of each order, and obtaining weighted average residuals after weighted average：
；
Wherein:
A weighted average residual error which is the residual value of the linear ionosphere model of each order;
The weight value is the weight value of the point to be measured;
The weight of the observation point;
residual errors of the to-be-measured points under the linear ionosphere model of each order are obtained;
The residual error of the observation point under the linear ionosphere model of each order is obtained.
Taking into account the use of points to be measuredTAC data of epoch, weight is small relative to observation point, can let/>Is 0.3,/>0.7.
S403: for a pair ofComparison, when/>And when i is determined to be the affiliated order of the optimal linear ionosphere model at the current moment T 0, the dynamic order determination of residual estimation is completed.
The method uses the weighted average result of the residual errors of the to-be-measured point and the observation point as the basis to compare the information such as curve gradient, ionosphere change rate and the like among models with different orders, and selects the most suitable linear ionosphere model order at the moment as the belonging order of the optimal linear ionosphere model.
S5: according to the belonging order of the optimal linear ionosphere model, iteratively updating TEC data to be measured points, including:
S501: and calculating TEC data of the to-be-measured point according to the belonging order of the optimal linear ionosphere model at the current moment T 0.
S502: taking TEC data to be measured as linear ionosphere model input parameters of the next epoch T 0 +1;
s503: repeating the steps to realize iterative updating of TEC data of the to-be-measured point.
The method effectively solves the problem that the traditional ionosphere modeling method is difficult to be applied to a traffic route system and linear areas with large spans, complex relief fluctuation and the like along the line industry and the like, and reduces the requirement of ionosphere interpolation on the distance between measuring stations and simultaneously ensures the product precision of the ionosphere model in extreme weather environment by considering the high-order gradient of ionosphere change. As the high-altitude change of the ionosphere is obtained, the ionosphere change condition with higher precision in the range can be obtained by using a few stations, and the dependence of ionosphere modeling on the station spacing is effectively reduced. The traditional ionosphere interpolation is only suitable for a certain range, the interpolation space has certain requirements (such as global equal large-scale interpolation space is not less than tens of kilometers, small-range area interpolation space cannot be higher than thousands of meters), if a model fitting result with high precision is to be kept, a measuring station is required to be increased, and then the measuring station space is reduced (namely, the application range and fitting precision (station space) cannot be obtained simultaneously).
Example 2:
The present embodiment provides a dynamic linear ionosphere modeling system based on linear distribution stations, the system being configured to implement the method of embodiment 1, comprising:
the selecting module is used for selecting a plurality of adjacent CORS stations which are linearly distributed on two sides of the point to be detected as observation points, and corresponds to S1 in the embodiment 1;
The acquisition module is used for acquiring an initial value of TEC data to be measured and corresponds to S2 in the embodiment 1;
the construction module is used for constructing linear ionosphere models of each order of corresponding orders according to the number of observation points, and corresponds to S3 in the embodiment 1;
The determining module is configured to perform residual estimation dynamic order determination on each order of the linear ionosphere model, determine an order of the optimal linear ionosphere model, and correspond to S4 in embodiment 1;
And the updating module is used for carrying out iterative updating on the TEC data to be measured according to the belonging order of the optimal linear ionosphere model, and corresponds to S5 in the embodiment 1.
Referring to fig. 3, statistics of errors between linear ionosphere model interpolation results and truth values during quiet and storm periods are shown. The model accuracy is found to be similar during calm and magnetic storm periods, the mean value is within 0.4 TECU, and the STD is within 1 TECU. The accuracy of the experimental model changes more stably in one day, and the accuracy of the experimental model is similar to that of other time periods in the noon, so that the used test data changes more uniformly in one day, the model order changes along with the change of the data, and the change trend of the ionosphere electron content is better fitted.
To further illustrate the advantages of the model presented herein over conventional linear interpolation models, the interpolation results of the present model are compared to conventional linear interpolation model results.
Table 1: distribution of interpolation results (POLY represents linear ionosphere fitting result, INTP represents traditional interpolation result) of two methods
The interpolation result distribution of the same site of the two models in the calm period and the magnetic storm period is shown in table 1, wherein POLY represents the result obtained by the ionosphere modeling method provided by the invention, and INTP represents the result obtained by using the two-site linear interpolation method. It can be seen from the table that more than 91% of the data were better than 1TECU using the POLY method during calm, more than 99.7% were better than 3TECU, and that the two data were found to be 80.04% and 98.84% respectively using the INTP method. The model error of more than 91% is better than that of 1TECU during the magnetic storm period by using the POLY method, and only 80.96% is needed by the INTP method, which proves that the method can effectively improve the traditional linear interpolation ionosphere fitting model accuracy.
Those skilled in the art will appreciate that all or part of the functions of the embodiments of the present invention may be implemented by means of hardware, or may be implemented by means of a computer program. When all or part of the functions in the above embodiments are implemented by means of a computer program, the program may be stored in a computer readable storage medium, and the storage medium may include: read-only memory, random access memory, magnetic disk, optical disk, hard disk, etc., and the program is executed by a computer to realize the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above can be realized. In addition, when all or part of the functions in the above embodiments are implemented by means of a computer program, the program may be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash disk, or a removable hard disk, and the program in the above embodiments may be implemented by downloading or copying the program into a memory of a local device or updating a version of a system of the local device, and when the program in the memory is executed by a processor.
The foregoing description of the invention has been presented for purposes of illustration and description, and is not intended to be limiting. Several simple deductions, modifications or substitutions may also be made by a person skilled in the art to which the invention pertains, based on the idea of the invention.
Claims (3)
1. A dynamic linear ionosphere modeling method based on linear distribution measuring stations is characterized in that:
The method comprises the following steps:
Selecting a plurality of adjacent CORS stations which are linearly distributed on two sides of a point to be detected as observation points;
Acquiring an initial value of TEC data to be measured;
constructing linear ionosphere models of all orders according to the number of observation points;
Carrying out residual estimation dynamic order determination on each order of linear ionosphere model, and determining the belonging order of the optimal linear ionosphere model;
according to the belonging order of the optimal linear ionosphere model, iteratively updating TEC data to be measured points;
According to the number of observation points, constructing each order linear ionosphere model with corresponding orders, which comprises the following steps:
Treating the TEC data of the ionosphere as a function of geographic location and time variation, comprising:
；
wherein:
TEC data for ionosphere;
is a position vector;
is a time variable;
at the same time, performing taylor series expansion on the function to obtain a function taylor series expansion, including:
；
wherein:
the latitude or longitude of the point to be measured;
I=1, 2, …, n, i is the latitude or longitude of the i-th observation point TEC data;
i is also the number of taylor series expansion;
When the number of observation points is n, the corresponding order is n/2, and the linear ionosphere model of each order is obtained, which comprises the following steps:
；
wherein:
Fitting errors for the model;
the step of carrying out residual estimation dynamic order determination on each order of linear ionosphere model to determine the belonging order of the optimal linear ionosphere model comprises the following steps:
inputting initial values of TEC data of to-be-measured points into each step of linear ionosphere model;
Calculating model residuals corresponding to the to-be-measured point and the observation point under the linear ionosphere model of each order, and obtaining weighted average residuals after weighted average Comprising:
；
wherein:
A weighted average residual error which is the residual value of the linear ionosphere model of each order;
The weight value is the weight value of the point to be measured;
The weight of the observation point;
residual errors of the to-be-measured points under the linear ionosphere model of each order are obtained;
residual errors of observation points under the linear ionosphere model of each order are obtained;
For a pair of Comparison, when/>Determining i as the affiliated order of the optimal linear ionosphere model at the current moment T 0, and finishing the dynamic order determination of residual estimation;
and carrying out iterative updating on TEC data to be measured according to the belonging order of the optimal linear ionosphere model, wherein the method comprises the following steps:
calculating TEC data of a to-be-measured point according to the belonging order of the optimal linear ionosphere model at the current moment T 0;
Taking TEC data to be measured as linear ionosphere model input parameters of the next epoch T 0 +1;
repeating the steps to realize iterative updating of TEC data of the to-be-measured point.
2. The dynamic linear ionosphere modeling method based on linear distribution measuring stations according to claim 1, wherein:
Obtaining an initial value of TEC data of a to-be-measured point comprises the following steps:
Obtaining an approximate value of TEC data of a to-be-measured point;
Obtaining TEC data of an observation point of the next epoch;
and taking the approximation value of the TEC data of the to-be-measured point and the TEC data of the observation point of the next epoch as initial values of the TEC data of the to-be-measured point, namely initial input parameters of the model.
3. The dynamic linear ionosphere modeling system based on linear distribution measuring stations is characterized in that:
the system for implementing the method of any one of claims 1-2, comprising:
the selecting module is used for selecting a plurality of adjacent CORS stations which are linearly distributed on two sides of the point to be detected as observation points;
the acquisition module is used for acquiring an initial value of TEC data to be measured;
the construction module is used for constructing linear ionosphere models of all orders corresponding to the number of observation points;
The determining module is used for carrying out residual estimation dynamic order determination on each order of linear ionosphere model and determining the number of the order of the optimal linear ionosphere model;
and the updating module is used for carrying out iterative updating on the TEC data to be measured point according to the belonging order of the optimal linear ionosphere model.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109828288A (en) * | 2019-01-23 | 2019-05-31 | 东南大学 | A kind of real-time ionospheric modeling and monitoring method based on region CORS |
| CN110377969A (en) * | 2019-06-27 | 2019-10-25 | 东南大学 | A kind of regional ionospheric layer delay Real-time modeling set method based on ELM |
| US10852439B1 (en) * | 2020-04-30 | 2020-12-01 | Beihang University | Global ionospheric total electron content prediction system |
| CN115808700A (en) * | 2023-02-09 | 2023-03-17 | 中国人民解放军战略支援部队航天工程大学 | Carrier tracking method for responding to ionospheric scintillation |
| CN115902964A (en) * | 2023-01-17 | 2023-04-04 | 中国测绘科学研究院 | Method for constructing ionized layer model in mixed order region |
| CN116562037A (en) * | 2023-05-16 | 2023-08-08 | 中国测绘科学研究院 | A Multilayer Adaptive High Precision Regional Ionospheric Modeling Method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3035080B1 (en) * | 2014-12-16 | 2022-08-31 | Trimble Inc. | Navigation satellite system positioning involving the generation of correction information |
| WO2020178984A1 (en) * | 2019-03-05 | 2020-09-10 | 三菱電機株式会社 | Information processing device and positioning augmentation information transmission method |
| CN111505702B (en) * | 2020-06-15 | 2023-08-11 | 华东交通大学 | An ionospheric tomography method based on truncated rays by vertical boundaries |
| CN114518586B (en) * | 2021-03-17 | 2024-04-30 | 山东科技大学 | GNSS precise single-point positioning method based on spherical harmonic expansion |
-
2023
- 2023-12-26 CN CN202311805467.9A patent/CN117454679B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109828288A (en) * | 2019-01-23 | 2019-05-31 | 东南大学 | A kind of real-time ionospheric modeling and monitoring method based on region CORS |
| CN110377969A (en) * | 2019-06-27 | 2019-10-25 | 东南大学 | A kind of regional ionospheric layer delay Real-time modeling set method based on ELM |
| US10852439B1 (en) * | 2020-04-30 | 2020-12-01 | Beihang University | Global ionospheric total electron content prediction system |
| CN115902964A (en) * | 2023-01-17 | 2023-04-04 | 中国测绘科学研究院 | Method for constructing ionized layer model in mixed order region |
| CN115808700A (en) * | 2023-02-09 | 2023-03-17 | 中国人民解放军战略支援部队航天工程大学 | Carrier tracking method for responding to ionospheric scintillation |
| CN116562037A (en) * | 2023-05-16 | 2023-08-08 | 中国测绘科学研究院 | A Multilayer Adaptive High Precision Regional Ionospheric Modeling Method |
Non-Patent Citations (4)
| Title |
|---|
| A New Model for Ionospheric Total Electron Content: The Impact of Solar Flux Proxies and Indices;Larisa P. Goncharenko 等;《Journal of Geophysical Research: Space Physics》;20210228;第126卷(第02期);全文 * |
| 基于BDS/GPS和经验模型的区域电离层建模;梁川 等;《空间科学学报》;20200115(第01期);全文 * |
| 基于多项式的区域电离层建模精度分析与验证;付伟 等;《北京测绘》;20181025(第10期);全文 * |
| 鞍山区域电离层模型构建及GNSS应用研究;刘馨蕊 等;《测绘科学》;20170331(第03期);全文 * |
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