CN116955885A

CN116955885A - Ionosphere model construction method, ionosphere model construction device, ionosphere model construction equipment and computer storage medium

Info

Publication number: CN116955885A
Application number: CN202210384346.0A
Authority: CN
Inventors: 付海洋; 眭韵; 汪登辉; 冯绍军; 徐丰; 金亚秋
Original assignee: Fudan University; Qianxun Spatial Intelligence Inc
Current assignee: Fudan University; Qianxun Spatial Intelligence Inc
Priority date: 2022-04-13
Filing date: 2022-04-13
Publication date: 2023-10-27

Abstract

The application discloses an ionosphere model construction method, device, equipment and a computer storage medium. The method comprises the following steps: acquiring a first observed quantity to obtain a first ionospheric delay quantity; obtaining N sub-ionosphere based on ionosphere division, and presetting function categories corresponding to the sub-ionosphere; acquiring priori electronic distribution data in an ionosphere physical model, and acquiring priori ionosphere delay amounts corresponding to each sub-ionosphere based on the priori electronic distribution data so as to acquire a scale factor representing a proportional relation between the priori ionosphere delay amounts of each sub-ionosphere; based on the first ionospheric delay amount and the scale factor, obtaining a virtual ionospheric delay amount of each sub-ionosphere; and constructing an ionospheric observation equation based on the function class, the virtual ionospheric delay amount and the first ionospheric delay amount to obtain an ionospheric model. The embodiment of the application is beneficial to improving the positioning precision based on the ionosphere model.

Description

Ionosphere model construction method, ionosphere model construction device, ionosphere model construction equipment and computer storage medium

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a computer storage medium for ionosphere model construction.

Background

The earth ionosphere is an important component of the earth atmosphere, and plasma formed by a large number of charged particles in the ionosphere influences the propagation of radio waves, and influences electromagnetic signals passing through the plasma to different degrees, such as reflection, refraction, scattering and absorption, are one of the main sources of space ranging errors of satellite navigation signals. The total electronic content (Total Electron Contents, TEC) of the ionized layer is one of the most important parameters describing the characteristics and the change of the ionized layer, and accurate acquisition of TEC distribution information is significant for deep research of physical characteristics and change rules of the ionized layer and improvement of positioning accuracy of a global navigation satellite system (Global Navigation Satellite System, GNSS).

However, in the related art, a model capable of reflecting the distribution of the ionized layer TEC more accurately is lacking, and it is difficult to effectively ensure the positioning accuracy.

Disclosure of Invention

The embodiment of the application provides an ionosphere model construction method, device, equipment and computer storage medium, which are used for solving the problem that the related technology lacks a model capable of accurately reflecting the distribution of ionosphere TECs and is difficult to effectively guarantee positioning accuracy.

In a first aspect, an embodiment of the present application provides a method for constructing an ionospheric model, where the method includes:

acquiring a first observed quantity to obtain a first ionospheric delay quantity;

obtaining N sub-ionosphere based on ionosphere division, presetting function types corresponding to each sub-ionosphere, wherein N is the preset number of layers, and N is an integer greater than 1;

acquiring priori electronic distribution data in an ionosphere physical model, and acquiring priori ionosphere delay amounts corresponding to all the sub-ionosphere based on the priori electronic distribution data so as to acquire a scale factor representing a proportional relation between the priori ionosphere delay amounts of all the sub-ionosphere;

based on the first ionospheric delay amount and the scale factor, obtaining virtual ionospheric delay amounts of all sub-ionosphere;

and constructing an ionospheric observation equation based on the function class, the virtual ionospheric delay amount and the first ionospheric delay amount to obtain an ionospheric model.

In a second aspect, an embodiment of the present application provides an ionosphere model building apparatus, including:

the first acquisition module is used for acquiring a first observed quantity so as to acquire a first ionospheric delay quantity;

the dividing module is used for obtaining N sub-ionosphere based on ionosphere division, presetting function categories corresponding to the sub-ionosphere, wherein N is the preset layer number, and N is an integer greater than 1;

The second acquisition module is used for acquiring priori electronic distribution data in the ionosphere physical model, and acquiring priori ionosphere delay amounts corresponding to all the sub-ionosphere based on the priori electronic distribution data so as to acquire a scale factor representing the proportional relation between the priori ionosphere delay amounts of all the sub-ionosphere;

the third acquisition module is used for obtaining the virtual ionosphere delay amount of each sub-ionosphere based on the first ionosphere delay amount and the scale factor;

and the construction module is used for constructing an ionospheric observation equation based on the function class, the virtual ionospheric delay amount and the first ionospheric delay amount to obtain an ionospheric model.

In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory storing computer program instructions;

the ionosphere model building method as in the first aspect is implemented when the processor executes the computer program instructions.

In a fourth aspect, embodiments of the present application provide a computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement the ionospheric model construction method as in the first aspect.

In a fifth aspect, embodiments of the present application provide a computer program product, instructions in which, when executed by a processor of an electronic device, cause the electronic device to perform the ionospheric model construction method as in the first aspect.

According to the ionosphere model construction method provided by the embodiment of the application, the first observed quantity is obtained to obtain the first ionosphere delay quantity; obtaining N sub-ionosphere based on ionosphere division, presetting function types corresponding to each sub-ionosphere, wherein N is the preset number of layers, and N is an integer greater than 1; acquiring priori electronic distribution data in an ionosphere physical model, and acquiring priori ionosphere delay amounts corresponding to all the sub-ionosphere based on the priori electronic distribution data so as to acquire a scale factor representing a proportional relation between the priori ionosphere delay amounts of all the sub-ionosphere; based on the first ionospheric delay amount and the scale factor, obtaining virtual ionospheric delay amounts of all sub-ionosphere; and constructing an ionospheric observation equation based on the function class, the virtual ionospheric delay amount and the first ionospheric delay amount to obtain an ionospheric model. According to the embodiment of the application, the ionosphere model comprising a plurality of layers of sub-ionosphere is constructed based on the first observed quantity and the priori electronic distribution data, so that the ionosphere model is more consistent with TEC distribution of the ionosphere, and has higher model precision, thereby being beneficial to improving the positioning precision based on the ionosphere model.

Drawings

In order to more clearly illustrate the technical solution of the embodiments of the present application, the drawings that are needed to be used in the embodiments of the present application will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.

FIG. 1 is a schematic flow chart of an ionosphere model building method according to an embodiment of the present application;

FIG. 2 is a flow chart of an ionosphere model building method in one specific application example;

FIG. 3 is a schematic structural diagram of an ionosphere model building device according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings and the detailed embodiments. It should be understood that the particular embodiments described herein are meant to be illustrative of the application only and not limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the application by showing examples of the application.

It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.

In order to solve the problems in the prior art, the embodiment of the application provides an ionosphere model construction method, an ionosphere model construction device, ionosphere model construction equipment and a computer storage medium. The ionosphere model construction method provided by the embodiment of the application is first described below.

Fig. 1 is a schematic flow chart of an ionosphere model building method according to an embodiment of the present application. As shown in fig. 1, the method includes:

step 101, obtaining a first observed quantity to obtain a first ionospheric delay quantity;

step 102, obtaining N sub-ionosphere based on ionosphere division, presetting function types corresponding to each sub-ionosphere, wherein N is a preset layer number, and N is an integer greater than 1;

step 103, acquiring prior electronic distribution data in the ionosphere physical model, and acquiring prior ionosphere delay amounts corresponding to all sub-ionosphere based on the prior electronic distribution data so as to acquire a scale factor representing a scale relation between the prior ionosphere delay amounts of all the sub-ionosphere;

104, obtaining virtual ionosphere delay amounts of all sub-ionosphere based on the first ionosphere delay amounts and the scale factors;

and 105, constructing an ionospheric observation equation based on the function class, the virtual ionospheric delay amount and the first ionospheric delay amount to obtain an ionospheric model.

The ionosphere model construction method provided by the embodiment of the application can be applied to electronic equipment, wherein the electronic equipment can be mobile electronic equipment, such as an intelligent mobile terminal or a tablet personal computer, or the electronic equipment can be non-mobile electronic equipment, such as a server, an industrial computer or various edge computing units and the like. The specific form of the electronic device is not limited in this embodiment.

In the application scenario of the global navigation satellite system (Global Navigation Satellite System, GNSS), the GNSS may include satellites and base stations (e.g. a base station of a ground-based augmentation system, etc.), and the electronic device may be a device in the base station, or may be a device independent of the base station, and data may be transmitted between the base station and the electronic device. For simplicity of explanation, the embodiments of the present application will be mainly described below by taking an example in which a base station includes an electronic device for performing an ionosphere model building method.

In step 101, the electronic device may obtain a first observed quantity to obtain a first ionospheric delay quantity.

In some examples, the first observables may be raw dual-frequency observables received by the base station via the receiver, navigation message data, or other types of data. In general, the original dual-band observables may be original observables transmitted to the base station by the satellite supporting dual-band communication, and the navigation message data may be message data describing operation state parameters of the navigation satellite broadcast to the base station by the navigation satellite.

The electronic device or other computing unit in the base station may process the first observed quantity to obtain a first ionospheric delay amount.

The above process of processing the first observed quantity to obtain the first ionospheric delay quantity may be implemented by means of the prior art. For example, the electronic device may extract the first ionospheric delay amount by using a non-differential non-combined precise single point positioning (Precise Point Positioning, PPP) technique based on the dual-frequency pseudo-range and the carrier observed value after removing the coarse difference with respect to the original dual-frequency observed value and the navigation message data.

The first ionospheric delay amount may be a delay amount generated by observation rays from satellites to base stations when penetrating the ionosphere, and may correspond to the first observation amount. In general, the ionospheric delay can be expressed by the total ionospheric electron content (Total Electron Contents, TEC).

In step 102, the electronic device may obtain N sub-ionosphere based on ionosphere division, and preset a function class corresponding to each sub-ionosphere.

The step can divide the ionized layer into a plurality of sub-ionized layers, and the observation rays can generate corresponding delay when penetrating through each sub-ionized layer. The delay amount generated by the observation ray in each sub-ionosphere can be determined by the function class correspondingly set for each sub-ionosphere.

By way of example, the functional class of each sub-ionosphere may be used to determine the vertical ionosphere delay amount of the corresponding sub-ionosphere at each latitude and longitude location, wherein the vertical ionosphere delay amount may be represented by the vertical ionosphere total electron content (Vertical Total Electron Content, VTEC).

In some examples, the class of function of the sub-ionosphere may be specifically a polynomial function form for determining VTEC, or a spherical harmonic form for individual VTECs. Since the ionosphere is divided into a plurality of sub-ionosphere based on the ionosphere division, the functional forms of the functional classes of the plurality of sub-ionosphere may be the same or different.

It is readily understood that in step 102, there may be associated function coefficients or orders, etc. in the function class corresponding to each sub-ionosphere, and that there may be differences in the function coefficients or orders in the function classes of the different sub-ionosphere. The specific values of these function coefficients or orders, which are typically one of the constituent elements of the constructed ionosphere model, are described in the examples below.

In step 103, a priori electronic distribution data in the ionosphere physical model is obtained, and a priori ionosphere delay amount corresponding to each sub-ionosphere is obtained based on the a priori electronic distribution data, so as to obtain a scale factor representing a proportional relation between the a priori ionosphere delay amounts of each sub-ionosphere.

In some possible implementations, the electronic device may obtain the prior electron density space-time distribution data by using an ionosphere physical model such as NeQuick2, and combine the dividing manner of the ionosphere in step 102 to obtain the prior ionosphere delay amount of each sub-ionosphere. For example, as shown above, the ionospheric delay may be represented by the total ionospheric electron content, and the a priori total electron content of each sub-ionosphere may be obtained from the a priori electron density space-time distribution data, i.e., the a priori ionosphere delay of each sub-ionosphere may be obtained.

Of course, in practical application, the prior electron distribution data may be obtained based on other manners, and the specific expression form of the prior electron distribution data may also be not limited to the prior electron density space-time distribution data.

To facilitate an understanding of the above scale factors, the following description is provided in connection with an example.

Based on a priori electron distributionThe data can obtain the priori ionosphere delay quantity of the ith sub-ionosphere in the N sub-ionosphere as TEC _i The scale factor characterizing the scale relationship between the a priori ionospheric delay amounts of the ith sub-ionosphere may be

Of course, in practical applications, the scale factor characterizing the scale relationship between the a priori ionospheric delay amounts of the ith sub-ionosphere may be TEC _i Ratio to the total delay of the ionosphere in the a priori electron distribution data. Alternatively, the scale factor characterizing the scale relationship between the a priori ionospheric delay amounts of the ith sub-ionosphere may also be TEC _i The ratio of the a priori ionospheric delay to the predetermined reference sub-ionosphere, etc., is not illustrated herein.

In step 104, the electronic device may obtain a virtual ionospheric delay amount for each sub-ionosphere based on the first ionospheric delay amount and the scaling factor.

The first ionospheric delay amount, which is typically the total delay amount produced by observation rays traversing the ionosphere, may be considered as the actual value observed by the base station, and the scale factor may represent an a priori proportional relationship between the delay amounts produced in the respective sub-ionosphere. Based on the first ionospheric delay amount and the scaling factor, a virtual ionospheric delay amount for each sub-ionosphere can be obtained.

For example, where the scale factor is TEC _i And under the condition of the ratio of the total delay amount of the ionized layers in the prior electron distribution data, the corresponding scale factors of the ionized layers can be multiplied by the delay amount of the first ionized layer to obtain the virtual ionized layer delay amount of the ionized layers.

Of course, in practical application, according to the determination manner of the scale factor, the virtual ionospheric delay amount of each sub-ionosphere can be obtained based on the first ionospheric delay amount and the scale factor in a corresponding manner, which is not exemplified herein.

In step 105, the electronic device may construct an ionospheric observation equation based on the function class, the virtual ionospheric delay amount, and the first ionospheric delay amount to obtain an ionospheric model.

As indicated above, the class of functions may be used to determine the amount of delay for each sub-ionosphere, which may be a theoretically calculated amount of delay. The virtual ionospheric delay can be considered to some extent as the delay of each sub-ionosphere based on a priori data, which can be considered to be close to the actual delay. The amount of delay determined based on each function class may be equal to or about equal to the amount of virtual ionospheric delay of the corresponding ionosphere.

Similarly, each function class may determine the theoretical delay amount of one sub-ionosphere, and the value added to the theoretical delay amounts corresponding to the N sub-ionosphere may be equal to or about equal to the first ionosphere delay amount.

An equality relationship may be established between the delay amounts determined for each function class and the virtual ionospheric delay amounts for the corresponding ionosphere, and an equality relationship may also be established between the delay amounts for the N sub-ionosphere and the first ionosphere delay amounts. These equality relationships can be considered as manifestations of ionospheric observation equations.

In step 105, there are some parameters to be solved in the equation relation, such as the coefficients of the functions in the function class. By solving these equality relations, the solving of the parameters to be solved can be completed, and then the ionosphere model is obtained.

In other words, the ionospheric model may correspond to an ionospheric observation equation in which the amount of delay of the observation ray in the ionosphere is a solution target, and the ionospheric model may have a function coefficient as a solution target.

The parameters in the ionosphere model used to calculate the amount of delay of an observation ray in the ionosphere may be determined, and in the case of known observation rays, the total amount of delay of the observation ray in penetrating the ionosphere may be determined from the ionosphere model, or the amount of delay of the observation ray in penetrating the respective sub-ionosphere may also be determined, etc. These determined delays can be used to correct the observed quantity of the observed rays and thus help to overcome the spatial ranging errors of the ionosphere on the satellite navigation signals.

In one embodiment, in step 101, the electronic device may obtain the first ionospheric delay amount in particular by the following way.

The navigation foundation enhancement system base station receives the original double-frequency observed quantity and navigation message data, eliminates the coarse difference, is based on the double-frequency pseudo-range observed quantity and the carrier phase observed quantity, and adopts the ionospheric observed quantity (corresponding to the first ionospheric delay quantity) obtained by a non-differential non-combination PPP algorithm. Wherein, the dual-frequency pseudo-range observed quantity and the carrier phase observed quantity are respectively expressed as:

wherein s, k, j represent the satellite, the receiver, and the frequency of communication between the two, respectively; c represents the speed of light;a pseudo-range observation at frequency j between satellite s and receiver k; />For the observed amount of carrier phase between satellite s and receiver k at frequency j; />The geometric distance from the satellite s to the phase center of the k antenna of the receiver; δt _k K clock-differences for the receiver; δt ^s Is satellite s clock difference; />Is a tropospheric delay; />Is ionospheric delay; alpha _j Is the frequency ratio, wherein:

f ₁ the frequency j corresponds to the carrier frequency, which is the frequency of the L1 carrier.

Pseudo-range hardware delay for the receiver on frequency j; />Pseudo-range hardware delay for satellites on frequency j;for the carrier phase deviation of the receiver at frequency j (hardDelay of the piece); />Is the carrier phase offset (hardware delay) of the satellite at frequency j. Lambda (lambda) _j Is the carrier wavelength at frequency j; n (N) _j Integer ambiguity for non-differential phase at frequency j; />For modeled errors including antenna phase center correction, antenna phase wrapping, relativistic effects, tidal correction, etc., it is assumed that the errors have been corrected into observations using empirical models; />Noise for pseudorange observations at frequency j; />Is the phase observation noise at frequency j. Where j=1 may correspond to an L1 carrier and j=2 may correspond to an L2 carrier.

The ionospheric delay may be represented by a TEC in the ionosphere, the spatial and temporal variations of which reflect the primary characteristics of the ionosphere, the first ionospheric delay amountCan be expressed as a form comprising TEC:

the formula may correspond to the ionospheric observation equation described above, where a represents the ionospheric propagation path integration constant, and in some examples, a may take the value 40.3, stec is the total electron content in the oblique direction (Slant Total Electron Content),for the total electron content, f, in the diagonal path between base station k and satellite s ₁ Is L1 carrier frequency, f ₂ DCB for the frequency of L2 carrier _k For the differential code deviation of receiver k, DCB ^s Is the differential code bias for satellite s.

The pseudo-range and phase observables can be expressed in the non-differential non-combination model, and the hardware delay difference value of the receiver and the satellite is expressed as two parts, namely frequency correlation and frequency independence:

Wherein the frequency dependent portionDelayed by ionosphere->Absorption, thus define->And +.>The hardware delay deviation of the receiver and the hardware delay deviation of each satellite respectively refer to the difference value of hardware delays among different frequencies; />And->The frequency independent delays of the receiver and the satellites, respectively.

After the ionospheric model is obtained by constructing an ionospheric observation equation based on the function class, the virtual ionospheric delay amount and the first ionospheric delay amount, the method further comprises:

obtaining an ionospheric delay modeling value based on an ionospheric model;

acquiring ionospheric delay amount actual measurement values respectively corresponding to the ionospheric delay amount modeling values based on the verification station;

constructing a loss function based on the ionospheric delay modeling value and the difference value of the ionospheric delay actual measurement value corresponding to the ionospheric delay modeling value, and optimizing parameters of an ionospheric model by adopting a machine learning optimization method;

wherein the parameters of the optimized ionosphere model include at least one of the number of layers of the sub-ionosphere, the layer height of each sub-ionosphere, the upper and lower bound heights of each sub-ionosphere, and the order of the ionosphere observation equation.

In some examples, the ionospheric model in the previous embodiment may be considered as an initially derived ionospheric model, in which the number of ionospheric layers may be preset, but there may be room for optimization. Similarly, in the initially obtained ionosphere model, the layer height of each sub-ionosphere, and the upper and lower bound heights of each sub-ionosphere may also be preset, which may also be space for optimization.

Of course, in connection with the above examples, the class of functions of the sub-ionosphere may be specifically a polynomial form for determining VTEC or a spherical harmonic form for individual VTEC, and expressions to these polynomial functions or spherical harmonics etc. may be included in the ionosphere observation equation, whereas the polynomial functions or spherical harmonics will typically have corresponding orders, which may correspond to the orders of the ionosphere observation equation. In the initially obtained ionospheric model, the order of the ionospheric observation equation may also be preset, which may also have space for optimization.

In this embodiment, the electronic device may obtain the ionospheric delay amount modeling value based on the ionospheric model. For simplicity of explanation, the ionospheric model that is not parameter optimized may be referred to as an initial ionospheric model hereinafter.

In the initial ionosphere model, the correlation coefficient is calculated based on the prior data, and accordingly, the initial ionosphere model can relatively roughly calculate the delay amount generated by the observation rays penetrating through the ionosphere, wherein the delay amount is the theoretically calculated delay amount and can correspond to the modeling value of the ionosphere delay amount.

In practical applications, the observation ray used to calculate the ionospheric delay amount modeling value may be actually present, for example, the observation ray may be a ray emitted by a satellite to a verification station at a certain time. The verification station may be the foundation enhancement system base station, or may be another type of base station, and may receive the observation beam through a receiver.

Accordingly, the ionospheric delay amount actual measurement values respectively corresponding to the ionospheric delay amount modeling values can be obtained based on the verification station.

The number of verification stations can be multiple, the satellite can also transmit multiple observation rays to the verification stations, correspondingly, the number of the ionospheric delay modeling values and the ionospheric delay actual measurement values can be multiple, and a one-to-one correspondence relationship exists between the ionospheric delay modeling values and the ionospheric delay actual measurement values.

In an ideal case, if the initial ionospheric model is in accordance with the actual distribution of the electron content of the ionosphere, the modeling value of the ionosphere delay amount and the measured value of the ionosphere delay amount corresponding to the modeling value are equal or approximate.

Of course, since there are more preset parameter values in the initial ionosphere model, such as the number of layers of the sub-ionosphere, etc., these preset parameter values may not be optimal values. Accordingly, the resulting ionospheric delay amount modeling value may differ from the corresponding ionospheric delay amount actual measurement value, which difference may be characterized by a loss value of the loss function.

In this embodiment, the electronic device may construct the loss function based on the ionospheric delay modeling value and the difference value of the ionospheric delay actual measurement value corresponding to the ionospheric delay modeling value, and optimize the parameter of the ionospheric model by using a machine learning optimization method.

As for the specific type of the machine learning optimization method used in the present embodiment, and the specific form of the loss function, the specific type and the specific form of the loss function are not particularly limited, and may be selected according to actual needs.

In combination with an example, by optimizing parameters of the ionosphere model, the loss value of the loss function may be made smaller than a preset threshold, and at this time, the optimized parameters may be obtained.

The ionosphere model with optimized parameters can be called a target ionosphere model, and compared with the initial ionosphere model, the target ionosphere model can calculate an ionosphere delay modeling value which is closer to the actual measurement value of the ionosphere delay.

As for the parameters of the optimized ionosphere model, at least one of the above-mentioned number of layers of the sub-ionosphere, the layer height of each sub-ionosphere, the upper and lower bound heights of each sub-ionosphere, and the order of the ionosphere observation equation may be used.

In this embodiment, a loss function is constructed based on the ionospheric delay modeling value and the difference value of the ionospheric delay actual measurement value corresponding to the ionospheric delay modeling value, and a machine learning optimization method is used to optimize parameters of the ionospheric model, so that the obtained optimized ionospheric model can determine a more accurate ionospheric delay, and positioning accuracy is improved.

Optionally, the function class is a vertical sub-ionosphere function, and the first ionosphere delay amount is an oblique ionosphere delay amount;

before the ionospheric model is obtained by constructing an ionospheric observation equation based on the function class, the virtual ionospheric delay amount and the first ionospheric delay amount, the method further comprises:

determining a mapping function of the mapping relation between the delay amount of the inclined ionosphere and the delay amount of the vertical ionosphere in each sub-ionosphere of the corresponding observation ray according to the puncture point position of the observation ray in each sub-ionosphere and the geometric characteristic of the observation ray;

based on the function class, the virtual ionospheric delay amount and the first ionospheric delay amount, constructing an ionospheric observation equation to obtain an ionospheric model, further comprising:

and constructing an ionospheric observation equation based on the vertical sub-ionospheric function, the virtual ionospheric delay amount, the mapping function and the first ionospheric delay amount serving as the oblique ionospheric delay amount to obtain an ionospheric model.

In connection with the above example, in the case of ionosphere division for ionosphere, data of each sub-ionosphere, such as the height and thickness of each sub-ionosphere, etc., may be known.

In the case of an ionosphere-penetrating observation rays, there may be a corresponding puncture point (Ionospheric Pierce Point, IPP) at each sub-ionosphere.

For example, in determining the ionosphere puncture point, an ionosphere multi-layer model assumption may be employed, i.e., assuming that all free electrons in the ionosphere are divided into N layers according to the altitude interval, the ith layer electron density is concentrated at altitude H _i Is an infinitely thin monolayer. As such, the ionosphere puncture points may be on an infinitely thin monolayer for each sub-ionosphere.

Of course, in other examples, the puncture points may be on a surface corresponding to an upper boundary of each sub-ionosphere, a surface corresponding to a lower boundary, a surface of a layer between the upper boundary and the lower boundary, or the like, which is not specifically limited herein.

In some embodiments, the geometry of the observation ray may be determined by the position of the satellite and the receiver, e.g., the geometry of the observation ray may include altitude and azimuth of the satellite relative to the receiver, and so on.

When the geometric features of the observation rays characterize the satellite as being directly above the receiver, the observation rays are directed perpendicularly into the respective sub-ionosphere, and the amount of ionospheric delay generated in the respective sub-ionosphere may be equal to the amount of vertical ionospheric delay. When the geometrical characteristic of the observation ray characterizes that the satellite is positioned obliquely above the receiver, the observation ray can be obliquely injected into each sub-ionosphere, and the ionosphere delay amount generated by the observation ray in each sub-ionosphere can be the oblique ionosphere delay amount.

In this embodiment, the function class of each sub-ionosphere may be a vertical sub-ionosphere function, i.e. the delay amount generated when the observation ray vertically penetrates the sub-ionosphere needs to be determined. However, in practical applications, the amount of delay that an observation ray produces in the ionosphere, such as the first ionosphere delay described above, is typically an oblique ionosphere delay.

In general, the tilt angle of the observation ray in each sub-ionosphere can be obtained based on the geometrical characteristics of the observation ray, and the mapping relationship between the bias ionosphere delay amount and the vertical ionosphere delay amount in each sub-ionosphere can be related to the tilt angle.

By way of example, the IPP of the observation ray at the ith sub-ionosphere may be referred to as IPP _i Based on the geometrical characteristics of the observation rays, the observation rays can be obtained in the IPP _i The angle between the position and the zenith direction is denoted as z _i '. The delay of the oblique ionosphere and the delay of the vertical ionosphere generated by the observation rays in the ith sub-ionosphere are respectively recorded asAnd->Then the following mapping exists:

the mapping function for representing the mapping relation between the bias ionospheric delay amount and the vertical ionospheric delay amount in the ith sub-ionosphere can be recorded as Then there are:

in this embodiment, the electronic device may construct an ionospheric observation equation based on the vertical sub-ionospheric function, the virtual ionospheric delay amount, the mapping function, and the first ionospheric delay amount as the bias ionospheric delay amount to obtain the ionospheric model.

In this embodiment, under the condition of the known mapping function, the vertical sub-ionosphere function, the virtual ionosphere delay amount and the first ionosphere delay amount may be combined to construct an ionosphere observation equation capable of regarding the delay amount generated when the observation ray vertically penetrates each sub-ionosphere, so as to obtain the ionosphere model.

In this embodiment, the ionospheric observation equation is constructed to obtain the ionospheric model by combining mapping functions representing the mapping relationship between the delay amount of the oblique ionosphere and the delay amount of the vertical ionosphere in each sub-ionosphere, which is helpful for more conveniently combining and applying the geometrical characteristics of the ionospheric model and the observation rays in the subsequent positioning application process, and efficiently determining the delay amount generated by the penetration of the observation straight line through the ionosphere, thereby improving the positioning precision and efficiency.

In some embodiments, the position, altitude E, and azimuth a of the satellites and receivers are known, and the latitude and longitude of the puncture point for each layer is determined according to the following formula

Wherein i represents the sub-ionosphere i-th layer, i.e. [1, N]N is the total number of layers of the ionosphere model, alpha _i The geocentric opening angle H representing the ith layer of puncture points _i For the height of the ith layer of the ionosphere, E, A are satellite altitude and azimuth, respectively, R is the earth radius, for example, R=6371 km, λ can be taken _k For the receiver longitude to be a function of the longitude,is the receiver latitude.

In addition, the angle z between the station star connection line (corresponding to the observation ray) and the zenith direction at the puncture point of the ith sub-ionosphere _i ' can be calculated by the following formula:

in the ionospheric observation equation mentioned above, the first ionospheric delay amountThe ionospheric observation equation can be expressed as a formula containing the form of TEC, taking into account the mapping function, which can be further written as:

the function value of the vertical sub-ionosphere function corresponding to the i-th sub-ionosphere, that is, the vertical ionosphere delay amount generated in the i-th sub-ionosphere by the observation ray, may be used. By way of example, the vertical sub-ionosphere function may be a polynomial function, spherical harmonic, spline function, or spherical cap harmonic.

Wherein the polynomial function has the expression:

wherein E is _mn Is a function coefficient which is a coefficient to be solved in the process of constructing the initial ionospheric model, and which may be a known value in the process of optimizing parameters of the ionospheric model. Is the measuring areaGeographical latitude of center point->For the geographical latitude of the puncture point IPP, +.>S ₀ Is the center point of the measuring areaAt the central time t of the period ₀ Lambda is the geographical longitude of the puncture point IPP and t is the observation time.

M and O are model orders, which are preset values during the process of constructing the initial ionosphere model, and which may be parameters to be optimized during the process of optimizing the parameters of the ionosphere model.

It is easy to understand that in the above formula, VTEC may be the vertical sub-ionosphere function corresponding to any sub-ionosphere, and for the vertical sub-ionosphere function corresponding to the ith sub-ionosphere, VTEC is the aboveAt this time, a->For the point of penetration IPP _i Lambda is the puncture point IPP _i Is a geographic longitude of (c).

The expression of the spherical harmonic is:

wherein A is _nm B (B) _nm As model coefficients, the function coefficients are coefficients to be solved in the process of constructing the above-mentioned initial ionospheric model, and the function coefficients may be known values in the process of optimizing parameters of the ionospheric model. n is n _max For model order, P _nm (cos phi) is the incompletely normalized n-order m-order luxLet de function. Phi and lambda are expressed as:

wherein lambda is _IPP Andis the geographic longitude and latitude at IPP, t is the current epoch time, lambda _M And->Geomagnetic longitude and latitude, lambda which are central points of the measuring areas _SUN Is the meridian longitude passing through the center of the earth and the sun.

Taking spherical harmonics as an example, the first ionospheric delay amountCan be further expressed as:

in the above formula, the subscript i of each polynomial may represent the ith sub-ionosphere.

In order to ensure that the model accords with physical interpretability, the proportional relation among the VTECs of each layer of the ionized layer calculated by the ionized layer physical model data is introduced as a model constraint condition. Specifically, the prior electron density space-time distribution data can be obtained by utilizing the ionosphere physical model such as Nequick2 and the like, and the prior of each layer is calculated according to the layering scheme determined before the ionosphereObtaining the prior VTEC duty ratio of each layer>Combining the current satellite with the oblique electron total content +.>Actual observed quantity, calculating the total content of oblique electrons in each layer of the ionized layer constrained by priori>The value is fused into a multi-layer modeling process as an added observed quantity to achieve a physical constraint effect:

as for the first ionospheric delay amount based on a priori dataThe principle of the delay amount of each sub-ionosphere obtained has been described in the above embodiments, and will not be described here again.

Optionally, constructing an ionospheric observation equation based on the vertical sub-ionospheric function, the virtual ionospheric delay amount, the mapping function, and the first ionospheric delay amount as the bias ionospheric delay amount to obtain an ionospheric model, including:

Modeling values with ionospheric delay amountEstablishing a first constraint relation equal to the first ionospheric delay amount, modeling the value +.> Establishing a second constraint relationship with the virtual ionospheric delay amount of the ith sub-ionosphere, which is equal to the first constraint relationship>For the vertical sub-ionosphere function corresponding to the ith sub-ionosphere,/the sub-ionosphere>The mapping function corresponding to the ith sub-ionosphere is used, i is an integer less than or equal to N;

establishing a third constraint relationship, wherein the third constraint relationship comprises: the sum of hardware delay deviation parameters associated with a plurality of observation satellites in the ionospheric observation equation is equal to 0;

establishing a fourth constraint relationship, the fourth constraint relationship comprising: the value of a hardware delay deviation parameter associated with a target satellite is 0, wherein the target satellite is an observation satellite which is not observed at each epoch moment in a plurality of observation satellites;

constructing an ionospheric observation equation based on the first constraint relationship, the second constraint relationship, the third constraint relationship and the fourth constraint relationship;

based on the ionosphere observation equation, solving parameters to be solved in the vertical sub-ionosphere functions corresponding to each sub-ionosphere to obtain an ionosphere model.

In combination with the example that the vertical sub-ionosphere function is a polynomial function, based on the first constraint relation and the second constraint relation, the constructed ionosphere observation equation can be expressed as follows:

Wherein use is made ofIndicating the total observed quantity of oblique electron content (corresponding to the first ionospheric delay quantity) on the 1 st satellite and receiver path, L is the total observed quantity, +.>The ionized layer each layer oblique electron total which is the N layer corresponding to the 1 st observed quantity and is constrained by prioriContent (virtual ionospheric delay amount corresponding to each sub-ionosphere), added as a new physical addition observation equation, +_>For the 1 st observation of the difference between the latitude at the puncture point location of the first layer and the geographical latitude of the central point of the zone,/o>1 st observation of longitude difference at puncture point location of first layer, M ₁ 、O ₁ The degree of latitude and longitude, respectively, of the layer 1 polynomial function, similarly, M _N 、O _N The degrees of latitude and longitude, respectively, of the nth layer polynomial function; right column vector, +.>To->For the first layer puncture point to correspond to the parameters to be estimated, Q is shared ₁ And can be obtained by calculating the order of longitude and latitude items; similarly, a->To->Indicating that the puncture point of the nth layer corresponds to the parameter to be estimated and shares Q _N And each.

For a constructed multi-layer model equation, estimating model parameters in real time by adopting a least square method or Kalman filtering, wherein the parameters to be estimated at the current moment comprise function parameters of each layer of the model:

The set X comprises parameters to be solved in the vertical sub-ionosphere function. Meanwhile, the set X may further include a hardware delay offset parameter, which may be a differential code offset (Differential Code Bias, DCB).

It will be readily appreciated that, as the subscript symbols indicate,hardware delay offset parameters generated in the 1 st satellite to prn satellite respectively,/->The hardware delay offset parameters generated in the 1 st to the R-th receivers, respectively.

For simplicity of explanation, the hardware delay variation parameter generated in any one satellite may be referred to as the hardware delay variation parameter associated with that satellite.

In this embodiment, in order to avoid rank deficiency of the ionospheric observation equation, a third constraint relationship and a fourth constraint condition may be further added, where on one hand, a sum of hardware delay deviation parameters associated with a plurality of observation satellites in the ionospheric observation equation is equal to 0, and on the other hand, a value of hardware delay deviation parameter associated with an observation satellite that is not observed at each epoch time in the plurality of observation satellites is 0.

In this embodiment, the ionosphere observation equation is constructed by establishing a plurality of constraint relations, which is conducive to reliably solving parameters to be solved in the ionosphere observation equation, and is further conducive to successful construction of the ionosphere model.

In some application scenarios, the plurality of observation satellites in the ionosphere observation equation may be, for example, satellites in a beidou navigation system or a GPS navigation system or other navigation systems, and accordingly, the third constraint condition may refer to a sum bit 0 of hardware delay deviation parameters of the satellites in each navigation system.

Optionally, the ionospheric delay modeling value includes a delay modeling value corresponding to the first satellite and a delay modeling value corresponding to the second satellite, and the ionospheric delay actual measurement value includes a delay actual measurement value corresponding to the first satellite and a delay actual measurement value corresponding to the second satellite;

constructing a loss function based on the difference value of the ionospheric delay quantity modeling value and the ionospheric delay quantity actual measurement value corresponding to the ionospheric delay quantity modeling value, specifically comprising:

acquiring a first difference value between a delay amount modeling value corresponding to a first satellite and a delay amount modeling value corresponding to a second satellite; acquiring a second difference value between the delay amount measured value corresponding to the first satellite and the delay amount measured value corresponding to the second satellite;

and taking the difference again between the first difference value and the second difference value to obtain a third difference value, and constructing a loss function based on the third difference value.

In this embodiment, to avoid the influence of the receiver DCB, the loss function may be constructed using differential observation data.

In combination with an example, a satellite with the best communication quality may be selected as a reference satellite in a preset area, where the reference satellite may be the first satellite or the second satellite. The difference between the observed quantity of other satellites and the observed quantity corresponding to the reference satellite can eliminate the DCB at the receiver end, and the DCB at the satellite end is obtained through parameter estimation:

wherein, the liquid crystal display device comprises a liquid crystal display device,DCB for the corresponding differential observations between satellite s and receiver k ^s The DCB is a satellite-side DCB which can be obtained through model parameter estimation ^ref Calculating the total content of differential bias ionosphere electrons for DCB of reference star>As a subsequent model performance evaluation observation:

further, an error of the observed quantity corresponding to each observed ray is defined as: the observed rays correspond to actual measurementSum model calculation +.>Difference value:

wherein, the liquid crystal display device comprises a liquid crystal display device,may correspond to the first difference, < >>Can correspond to the second difference, < >>May correspond to the third difference described above.

On this basis, in one example, the loss function may be defined as the modeling error of K verification stationsMean of root mean square error (Root Mean Square Error, RMSE) values, i.e.:

wherein Loss is the Loss value of the Loss function, M is the order of the ionosphere model function currently set, N is the number of the ionosphere model layers currently set, K is the number of verification stations, abs is the rounding operation, Centralizing for verificationIonosphere differential observables corresponding to observables of the third verification station, and +.>And (3) calculating the ionospheric differential observed quantity corresponding to the observed quantity by using the current parameter configuration model. When the parameters of the ionosphere are optimized based on the loss function, the optimized parameters can be M and N, or the upper boundary of the height of the ith sub-ionosphereHeight lower bound->Puncture point height of ith sub-ionosphere +.>(corresponding to the height of the sub-ionosphere).

Of course, in practical application, the specific form of the loss function may be adjusted according to needs, for example, the RMSE may be replaced by a standard deviation, or a process of abs rounding operation may be omitted, which is not illustrated herein.

Based on the above examples, the present embodiment constructs the loss function based on the third difference, so that the loss value of the loss function is not affected by the receiver DCB, which is helpful to reduce the optimization difficulty of the ionospheric model optimization process.

In some embodiments of the present invention, in some embodiments,and +.>Is constrained by an external physical model. Specifically, the peak heights HmE, hmF1, hmF and the thickness information of each layer of the ionosphere E layer, F1 layer, F2 layer can be obtained by using a NeQuick2 model or the like as the upper limit of the height of each layer of the parameters to be optimized +. >Height lower bound->The height of the puncture point of each layer of model>Is set to be a constant value. />

In some embodiments, the ionosphere model optimization process divides the base stations within the region into training and validation sets according to the K-fold cross validation principle. Initializing model parameters, including model layer number N, model function order of each layer, height upper boundary and height lower boundary of each layer, and puncture point height of each layer. And constructing an ionospheric observation equation by using the training set data to obtain an ionospheric model, constructing a loss function by using the verification set data, and optimizing relevant parameters in the ionospheric model according to the loss function. .

In combination with some examples, in the process of optimizing the ionosphere model, a bayesian optimization method can be used to obtain model parameters corresponding to the objective function when the objective function obtains the global optimal solution. In the process, a Gaussian process is used as a proxy model in Bayesian optimization, the priori of an objective function is assumed to be the Gaussian process, and posterior distribution of the function can be obtained according to Bayesian theorem after certain tests are carried out; then consider where the next test point is to collect data further; and then, performing a test to obtain posterior distribution of the updated proxy model after data until the iteration times are reached.

Optionally, after constructing the loss function based on the ionospheric delay modeling value and the difference value of the ionospheric delay actual measurement value corresponding to the ionospheric delay modeling value, and optimizing the parameter of the ionospheric model by adopting a machine learning optimization method, the method further comprises:

acquiring each grid in the ionosphere model association area after parameter optimization;

and determining target vertical ionospheric delay amounts respectively corresponding to the grids based on the parameter-optimized ionospheric model.

In combination with the above example of a vertical sub-ionosphere function, in the polynomial function, the VTEC calculation process is applied to the geographic longitude and latitude of the center point of the region; in spherical harmonics, the VTEC calculation process is applied to geomagnetic longitude and latitude of the center point of the measurement region. It can be seen that the VTEC of each sub-ionosphere in the ionosphere model may be associated with the center point of the region.

The central point of the measurement area can be regarded as the central point of the ionosphere model association area after parameter optimization to a certain extent, the ionosphere model association area is divided, a plurality of grids can be obtained, and each grid has corresponding geographic longitude and latitude or geomagnetic longitude and latitude, so that based on the ionosphere model after parameter optimization, the electronic equipment can determine the target vertical ionosphere delay amount corresponding to each grid respectively.

In combination with some application scenarios, under the condition that each grid corresponds to the target vertical ionospheric delay amount, the corresponding relationship between the grid and the target vertical ionospheric delay amount can be sent to the user terminal by the electronic device. When the user terminal is positioned in one grid, the user terminal can determine the target vertical ionospheric delay amount based on the corresponding relation, or further determine the target oblique ionospheric delay amount by combining the angle relation between the satellite and the user terminal, and the user terminal can eliminate or reduce the interference of the ionosphere on positioning according to the target vertical ionospheric delay amount or the target oblique ionospheric delay amount, so that the positioning accuracy is improved.

In one embodiment, the electronic device may test the optimal parameter configuration of the regional multilayer ionosphere model monthly or seasonal, and construct a multilayer ionosphere model that best meets the physical constraint in the region by using the optimal parameter configuration of the model in the period, thereby calculating the ionosphere delay amount at the standard lattice point, and storing and broadcasting the ionosphere delay amount in a lattice form to the user. The broadcast format employs an ionospheric map exchange format (The IONosphere Map Exchange Format, IONEX).

As shown in fig. 2, fig. 2 is a schematic flow chart of an ionosphere model building method in a specific application example. The application example can be specifically based on a navigation foundation augmentation system for constructing an ionosphere model. The following describes the ionosphere construction process in detail.

Firstly, a base station of a navigation foundation enhancement system receives original double-frequency observed quantity (corresponding to base station real-time observed data, including L1 frequency band observed data and L2 frequency band observed data) and navigation text data (such as orbit information, clock error data and the like), and calculates satellite position and altitude angle after removing rough differences; based on the double-frequency pseudo-range and the carrier observation value, the ionosphere delay observation value is extracted by adopting a non-differential non-combination PPP method.

Secondly, dividing the ionized layer into multiple layers according to the set layer number and the height, and determining the IPP coordinates of puncture points of each layer and corresponding projection functions, so that the oblique ionized layer delay STEC at the IPP position of each layer is converted into the vertical ionized layer delay VTEC; based on the ionosphere multi-layer thin shell hypothesis, selecting a proper function form to construct an ionosphere multi-layer VTEC model, and introducing ionosphere physical model data to restrict the proportional relationship among the ionosphere layers VTEC so as to ensure that the model accords with a physical rule;

and then, using a plurality of verification station model errors as loss functions, and adopting a Bayesian optimization method and the like to carry out optimal configuration on parameters such as the number of ionosphere layers, the function order, the layering height and the like in the model.

And finally, constructing an ionosphere model of physical constraint by utilizing optimal model parameter configuration, calculating an ionosphere VTEC at grid points, storing in a grid form and broadcasting to regional users.

Compared with a single-layer ionosphere TEC model, the ionosphere model obtained by the method has higher precision and better physical interpretability, is used as an ionosphere constraint condition in a PPP algorithm, and has a remarkable improvement effect on single-frequency and multi-frequency PPP user positioning precision and convergence time. The ionosphere model obtained by the method can obtain three-dimensional space-time change characteristics of the ionosphere on the premise of ensuring modeling precision and low complexity, provides a technical basis for deep understanding of ionosphere change mechanisms, establishment of high-precision ionosphere report and forecast models and the like, and has remarkable improvement effect on single-frequency and multi-frequency PPP user positioning precision and convergence time.

As shown in fig. 3, the embodiment of the present application further provides an ionosphere model building device, where the device includes:

a first obtaining module 301, configured to obtain a first observed quantity to obtain a first ionospheric delay amount;

the dividing module 302 is configured to obtain N sub-ionosphere based on ionosphere division, preset function types corresponding to each sub-ionosphere, N is a preset number of layers, and N is an integer greater than 1;

a second obtaining module 303, configured to obtain a priori electronic distribution data in the ionosphere physical model, obtain a priori ionosphere delay amount corresponding to each sub-ionosphere based on the a priori electronic distribution data, and obtain a scale factor characterizing a scale relationship between the a priori ionosphere delay amounts of each sub-ionosphere;

A third obtaining module 304, configured to obtain a virtual ionospheric delay amount of each sub-ionosphere based on the first ionospheric delay amount and the scaling factor;

a third obtaining module 305 is configured to construct an ionospheric observation equation based on the function class, the virtual ionospheric delay amount and the first ionospheric delay amount to obtain an ionospheric model.

Optionally, the ionosphere model building device may further include:

a fourth acquisition module, configured to acquire an ionospheric delay amount modeling value based on an ionospheric model;

a fifth acquisition module, configured to acquire ionospheric delay amount actual measurement values respectively corresponding to the ionospheric delay amount modeling values based on the verification station;

the optimizing module is used for constructing a loss function based on the ionospheric delay modeling value and the difference value of the ionospheric delay actual measurement value corresponding to the ionospheric delay modeling value, and optimizing parameters of the ionospheric model by adopting a machine learning optimizing method;

The ionosphere model building device may further include:

the first determining module is used for determining a mapping function of the corresponding observation ray, which characterizes the mapping relation between the delay amount of the inclined ionosphere and the delay amount of the vertical ionosphere in each sub-ionosphere, according to the puncture points of the observation ray in each sub-ionosphere and the geometric characteristics of the observation ray;

accordingly, the third obtaining module 305 is specifically configured to:

Optionally, the third obtaining module 305 includes:

a first establishing unit for modeling values with ionospheric delay amountEstablishing a first constraint relation equal to the first ionospheric delay amount, modeling the value +.>Establishing a second constraint relationship with the virtual ionospheric delay amount of the ith sub-ionosphere, which is equal to the first constraint relationship>For the vertical sub-ionosphere function corresponding to the ith sub-ionosphere,/the sub-ionosphere>The mapping function corresponding to the ith sub-ionosphere is used, i is an integer less than or equal to N;

the second establishing unit is configured to establish a third constraint relationship, where the third constraint relationship includes: the sum of hardware delay deviation parameters associated with a plurality of observation satellites in the ionospheric observation equation is equal to 0;

A third establishing unit, configured to establish a fourth constraint relationship, where the fourth constraint relationship includes: the value of a hardware delay deviation parameter associated with a target satellite is 0, wherein the target satellite is an observation satellite which is not observed at each epoch moment in a plurality of observation satellites;

the first construction unit is used for constructing an ionospheric observation equation based on the first constraint relation, the second constraint relation, the third constraint relation and the fourth constraint relation;

and the solving unit is used for solving parameters to be solved in the vertical sub-ionosphere functions corresponding to each sub-ionosphere based on the ionosphere observation equation so as to obtain an ionosphere model.

the optimizing module specifically comprises:

an acquisition unit configured to acquire a first difference between a delay amount modeling value corresponding to a first satellite and a delay amount modeling value corresponding to a second satellite; acquiring a second difference value between the delay amount measured value corresponding to the first satellite and the delay amount measured value corresponding to the second satellite;

And the second construction unit is used for taking the difference between the first difference value and the second difference value again to obtain a third difference value, and constructing a loss function based on the third difference value.

Optionally, the ionosphere model building device may further include:

a sixth acquisition module, configured to acquire each grid in the ionosphere model association area after parameter optimization;

and the second determining module is used for determining target vertical ionospheric delay amounts respectively corresponding to the grids based on the ionospheric model after parameter optimization.

It should be noted that, the ionosphere model building device is a device corresponding to the ionosphere model building method, and all implementation manners in the method embodiment are applicable to the device embodiment, so that the same technical effects can be achieved.

Fig. 4 shows a schematic hardware structure of an electronic device according to an embodiment of the present application.

The electronic device may comprise a processor 401 and a memory 402 in which computer program instructions are stored.

In particular, the processor 401 described above may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits implementing embodiments of the present application.

Memory 402 may include mass storage for data or instructions. By way of example, and not limitation, memory 402 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. Memory 402 may include removable or non-removable (or fixed) media, where appropriate. Memory 402 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 402 is a non-volatile solid state memory.

In particular embodiments, memory 402 may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to methods in accordance with aspects of the present disclosure.

The processor 401 implements any of the ionosphere model building methods of the above embodiments by reading and executing computer program instructions stored in the memory 402.

In one example, the electronic device may also include a communication interface 403 and a bus 410. As shown in fig. 4, the processor 401, the memory 402, and the communication interface 403 are connected by a bus 410 and perform communication with each other.

The communication interface 403 is mainly used to implement communication between each module, device, unit and/or apparatus in the embodiment of the present application.

Bus 410 includes hardware, software, or both, coupling components of the online data flow billing device to each other. By way of example, and not limitation, the buses may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. Bus 410 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.

In addition, in combination with the ionosphere model building method in the above embodiment, the embodiment of the application may be implemented by providing a computer storage medium. The computer storage medium has stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the ionosphere model building methods of the above embodiments.

It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present application.

The functional blocks shown in the above block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.

It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.

Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present application, and they should be included in the scope of the present application.

Claims

1. An ionosphere model construction method, comprising:

obtaining N sub-ionosphere based on ionosphere division, presetting function categories corresponding to the sub-ionosphere, wherein N is a preset layer number, and N is an integer greater than 1;

acquiring priori electronic distribution data in an ionosphere physical model, and acquiring priori ionosphere delay amounts corresponding to each sub-ionosphere based on the priori electronic distribution data so as to acquire a scale factor representing a proportional relation between the priori ionosphere delay amounts of each sub-ionosphere;

Based on the first ionospheric delay amount and the scale factor, obtaining a virtual ionospheric delay amount of each sub-ionosphere;

2. The method of claim 1, wherein after constructing an ionospheric observation equation based on the function class, the virtual ionospheric delay amount, and the first ionospheric delay amount to obtain an ionospheric model, the method further comprises:

obtaining an ionospheric delay modeling value based on the ionospheric model;

acquiring ionospheric delay amount actual measurement values respectively corresponding to the ionospheric delay amount modeling values based on a verification station;

constructing a loss function based on the ionospheric delay modeling value and the difference value of the ionospheric delay actual measurement value corresponding to the ionospheric delay modeling value, and optimizing parameters of the ionospheric model by adopting a machine learning optimization method;

wherein the parameters of the ionosphere model that are optimized include at least one of the number of layers of the sub-ionosphere, the layer height of each of the sub-ionosphere, the upper and lower bound heights of each of the sub-ionosphere, and the order of the ionosphere observation equation.

3. The method of claim 2, wherein the class of functions is a vertical sub-ionospheric function, and the first ionospheric delay amount is a bias ionospheric delay amount;

determining a mapping function corresponding to the observation rays and representing a mapping relation between the delay amount of the inclined ionosphere and the delay amount of the vertical ionosphere in each sub-ionosphere according to the positions of the puncture points of the observation rays in each sub-ionosphere and the geometric characteristics of the observation rays;

the step of constructing an ionospheric observation equation based on the function class, the virtual ionospheric delay amount and the first ionospheric delay amount to obtain an ionospheric model, further comprises:

and constructing an ionospheric observation equation based on the vertical sub-ionospheric function, the virtual ionospheric delay amount, the mapping function and the first ionospheric delay amount serving as an oblique ionospheric delay amount to obtain an ionospheric model.

4. A method according to claim 3, wherein said constructing an ionospheric observation equation based on said vertical sub-ionospheric function, said virtual ionospheric delay amount, said mapping function, and said first ionospheric delay amount as a bias ionospheric delay amount to obtain an ionospheric model comprises:

Modeling values with ionospheric delay amountEstablishing a first constraint relation equal to the first ionospheric delay amount, and modeling the value +.> Establishing a second constraint relation equal to the virtual ionospheric delay amount of the ith sub-ionosphere, said +.>For the vertical sub-ionosphere function corresponding to the ith sub-ionosphere,/the sub-ionosphere>The mapping function corresponding to the ith sub-ionosphere is used, i is an integer less than or equal to N;

establishing a third constraint relationship, the third constraint relationship comprising: the sum of hardware delay deviation parameters associated with a plurality of observation satellites in the ionospheric observation equation is equal to 0;

establishing a fourth constraint relationship, the fourth constraint relationship comprising: the value of the hardware delay deviation parameter associated with a target satellite is 0, the target satellite being an observed satellite of the plurality of observed satellites that is not observed at each epoch time;

and solving parameters to be solved in the vertical sub-ionosphere functions corresponding to each sub-ionosphere based on the ionosphere observation equation so as to obtain an ionosphere model.

5. The method of claim 2, wherein the ionospheric delay modeling values include a delay modeling value corresponding to a first satellite and a delay modeling value corresponding to a second satellite, the ionospheric delay actual values include a delay actual value corresponding to the first satellite and a delay actual value corresponding to the second satellite;

the constructing a loss function based on the ionospheric delay modeling value and the difference value of the ionospheric delay actual measurement value corresponding to the ionospheric delay modeling value specifically includes:

acquiring a first difference value between the delay amount modeling value of the corresponding first satellite and the delay amount modeling value of the corresponding second satellite; acquiring a second difference value between the delay amount measured value of the corresponding first satellite and the delay amount measured value of the corresponding second satellite;

and taking the difference between the first difference value and the second difference value again to obtain a third difference value, and constructing a loss function based on the third difference value.

6. The method of claim 2, wherein the constructing a loss function based on the ionospheric delay modeling value and a difference value between the ionospheric delay modeling value and the ionospheric delay actual measurement value corresponding to the ionospheric delay modeling value, and wherein the optimizing the parameters of the ionospheric model by using a machine learning optimization method further comprises:

7. An ionosphere model building apparatus, the apparatus comprising:

the dividing module is used for obtaining N sub-ionosphere based on ionosphere division, presetting function categories corresponding to the sub-ionosphere, wherein N is a preset layer number, and N is an integer greater than 1;

the second acquisition module is used for acquiring priori electronic distribution data in the ionosphere physical model, and acquiring priori ionosphere delay amounts corresponding to the sub-ionosphere based on the priori electronic distribution data so as to acquire a scale factor representing the proportional relation between the priori ionosphere delay amounts of the sub-ionosphere;

a third obtaining module, configured to obtain a virtual ionospheric delay amount of each sub-ionosphere based on the first ionospheric delay amount and the scaling factor;

8. An electronic device, the device comprising: a processor and a memory storing computer program instructions;

the ionosphere model building method of any of claims 1-6 when executed by the processor.

9. A computer readable storage medium, having stored thereon computer program instructions which, when executed by a processor, implement the ionosphere model building method of any of claims 1-6.

10. A computer program product, characterized in that instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the ionosphere model building method according to any of claims 1-6.