Ionospheric modeling method and apparatus, device, and storage medium

Abstract

Disclosed in the present invention are an ionospheric modeling method and apparatus, a device, and a storage medium. The method comprises: on the basis of an ionospheric model corresponding to historical GNSS observation data, calculating an ionospheric delay corresponding to the historical GNSS observation data, and then separating the coupled ionospheric delay and differential code bias information; separating a receiver differential code bias and a satellite differential code bias in the differential code bias information; fitting variation laws of the receiver differential code bias and the satellite differential code bias, estimating a predicted receiver differential code bias value and a predicted satellite differential code bias value within a time period for modeling; in the GNSS observation data within the time period for modeling, removing the predicted receiver differential code bias value and the predicted satellite differential code bias value, and then on the basis of the GNSS observation data having undergone removal operation, constructing an ionospheric model corresponding to the time period for modeling. The present invention can improve the accuracy of a constructed ionospheric model.

Description

Ionospheric modeling method, device and equipment and storage medium TECHNICAL FIELD

[0001] The application relates to the technical field of GNSS precise data processing, in particular to an ionospheric modeling method, device and equipment and storage medium. BACKGROUND

[0002] In the GNSS positioning process, the error caused by the satellite signal passing through the ionosphere is an important factor restricting the positioning accuracy and the positioning convergence time, so an accurate ionospheric model can provide more accurate ionospheric initial values and weights for the positioning filtering process, thereby improving the starting positioning accuracy and shortening the positioning convergence time.

[0003] The receiver differential code bias and the satellite differential code bias are important factors restricting the model accuracy in the ionospheric modeling process. Since the differential code bias and the ionospheric delay obtained from the GNSS observation value are coupled with each other, the differential code bias cannot be separated, therefore, in the modeling process, the differential code bias is separated from the total electron content by assuming that the differential code bias is constant in a short time (one day), and the assumption is established on the premise that the receiver state and the environment are stable. However, in the actual production process, the differential code bias cannot be guaranteed to be stable due to the influence of factors such as the temperature and humidity of the receiver environment, the space radiation in the space environment of the satellite, the service life of the receiver and the satellite, and the like, thereby leading to low accuracy of the constructed ionospheric model. SUMMARY

[0004] The application provides an ionospheric modeling method, device and equipment and storage medium to solve the technical problem that the differential code bias and the ionospheric delay obtained from the GNSS observation value are coupled with each other, the differential code bias cannot be separated, and the accuracy of the constructed ionospheric model is low.

[0005] To solve the above technical problem, the application embodiment provides an ionospheric modeling method, comprising:

[0006] Obtaining a plurality of historical GNSS observation data of a to-be-modeled area in a preset time period; wherein the historical GNSS observation data comprises historical pseudorange observation data and historical carrier observation data of all base station receivers;

[0007] Calculating the coupled ionospheric delay and differential code bias information corresponding to each historical GNSS observation data; wherein the differential code bias information comprises a receiver differential code bias and a satellite differential code bias;

[0008] According to the ionospheric model corresponding to the historical GNSS observation data, ionospheric delay corresponding to the historical GNSS observation data is calculated, and then coupled ionospheric delay and differential code bias information are separated;

[0009] According to the separated differential code bias information, receiver differential code bias and satellite differential code bias in the differential code bias information are separated;

[0010] According to the separated receiver differential code bias, the variation law of the receiver differential code bias is fitted, and according to the separated satellite differential code bias, the variation law of the satellite differential code bias is fitted;

[0011] According to the variation law of the receiver differential code bias, the receiver differential code bias prediction value in the to-be-modeled period is estimated, and according to the variation law of the satellite differential code bias, the satellite differential code bias prediction value in the to-be-modeled period is estimated;

[0012] According to the receiver differential code bias prediction value and the satellite differential code bias prediction value, the receiver differential code bias prediction value and the satellite differential code bias prediction value are removed from the GNSS observation data in the to-be-modeled period, and then according to the removed GNSS observation data, the corresponding ionospheric model in the to-be-modeled period is constructed.

[0013] As a preferred scheme, the ionospheric modeling method further comprises:

[0014] According to the constructed ionospheric model and the corresponding GNSS observation data, the ionospheric delay in the GNSS observation data is removed, and then GNSS positioning is performed according to the removed GNSS observation data.

[0015] As a preferred scheme, the calculation of the coupled ionospheric delay and differential code bias information corresponding to each historical GNSS observation data comprises:

[0016] According to the historical pseudorange observation data, the corresponding non-geometric combined pseudorange observation value is calculated, and according to the historical carrier observation data, the corresponding non-geometric combined carrier observation value is calculated;

[0017] The mean value of the sum of the non-geometric combined pseudorange observation value and the non-geometric combined carrier observation value in the same preset arc segment is calculated, and then the coupled ionospheric delay and differential code bias information corresponding to each historical GNSS observation data is calculated according to the mean value and the non-geometric combined carrier observation value.

[0018] As a preferred scheme, the coupled ionospheric delay and differential code bias information is:

[0019] wherein, denotes the geometry-free combined carrier observations in meters, STEC denotes the ionospheric delay, f1 denotes the frequency corresponding to the geometry-free combined pseudorange observations, f2 denotes the frequency corresponding to the geometry-free combined carrier observations, c denotes the speed of light in vacuum, DCB r,1|2 denotes the receiver differential code bias of the receiver at frequency f1 and frequency f2, denotes the satellite differential code bias of the satellite at frequency f1 and f2, <P4+φ4> arc denotes the mean of the sum of the geometry-free combined pseudorange observations and the geometry-free combined carrier observations within the same preset arc segment.

[0020] As a preferred solution, the calculating the ionospheric delay corresponding to the historical GNSS observation data according to the ionospheric model corresponding to the historical GNSS observation data, and then separating the coupled ionospheric delay and differential code bias information comprises:

[0021] determining whether the historical GNSS observation data has a corresponding ionospheric model, when it is determined that the historical GNSS observation data has a corresponding ionospheric model, calculating the piercing point coordinates of the historical GNSS observation data at the height of the corresponding ionospheric model according to the ionospheric model corresponding to the historical GNSS observation data;

[0022] calculating the vertical path value corresponding to the historical GNSS observation data according to the piercing point coordinates;

[0023] calculating the ionospheric projection function value corresponding to the historical GNSS observation data, and then converting the vertical path value into a corresponding slant path value according to the ionospheric projection function value;

[0024] taking the slant path value as the ionospheric delay corresponding to the historical GNSS observation data, and then separating the coupled ionospheric delay and differential code bias information.

[0025] As a preferred solution, the calculating the ionospheric delay corresponding to the historical GNSS observation data according to the ionospheric model corresponding to the historical GNSS observation data, and then separating the coupled ionospheric delay and differential code bias information further comprises:

[0026] when it is determined that the historical GNSS observation data does not have a corresponding ionospheric model, assuming that the receiver differential code bias and the satellite differential code bias corresponding to the historical GNSS observation data are the same within a preset time period, and then solving the ionospheric model corresponding to the historical GNSS observation data according to a preset solving equation of the ionospheric model and the differential code bias;

[0027] According to the ionosphere model corresponding to the historical GNSS observation data, the piercing point coordinates of the historical GNSS observation data on the height of the corresponding ionosphere model are calculated;

[0028] According to the piercing point coordinates, the vertical path values corresponding to the historical GNSS observation data are calculated;

[0029] The ionosphere projection function values corresponding to the historical GNSS observation data are calculated, and then the vertical path values are converted into corresponding slant path values according to the ionosphere projection function values;

[0030] The slant path values are taken as the ionosphere delay corresponding to the historical GNSS observation data, and then the coupled ionosphere delay and differential code bias information are separated.

[0031] As a preferred solution, the receiver differential code bias and satellite differential code bias in the differential code bias information are separated according to the separated differential code bias information, which comprises:

[0032] According to the separated differential code bias information, the sum of the satellite differential code bias in the differential code bias information is zero, and the corresponding receiver differential code bias is solved, and then the receiver differential code bias and satellite differential code bias in the differential code bias information are separated.

[0033] On the basis of the above-mentioned embodiments, another embodiment of the present application provides an ionosphere modeling device, which comprises a historical observation data acquisition module, a coupled data calculation module, a coupled data separation module, a differential code bias information separation module, a change rule fitting module, a differential code bias estimation module and an ionosphere model construction module;

[0034] The historical observation data acquisition module is used for acquiring a plurality of historical GNSS observation data of a to-be-modeled region in a preset time period; wherein the historical GNSS observation data comprises historical pseudo-range observation data and historical carrier observation data of all base station receivers;

[0035] The coupled data calculation module is used for calculating the coupled ionosphere delay and differential code bias information corresponding to each historical GNSS observation data; wherein the differential code bias information comprises receiver differential code bias and satellite differential code bias;

[0036] The coupled data separation module is used for calculating the ionosphere delay corresponding to the historical GNSS observation data according to the ionosphere model corresponding to the historical GNSS observation data, and then separating the coupled ionosphere delay and differential code bias information;

[0037] The differential code bias information separation module is configured to separate the receiver differential code bias and the satellite differential code bias in the differential code bias information according to the separated differential code bias information.

[0038] The variation law fitting module is configured to fit the variation law of the receiver differential code bias according to the separated receiver differential code bias, and fit the variation law of the satellite differential code bias according to the separated satellite differential code bias.

[0039] The differential code bias estimation module is configured to estimate the receiver differential code bias pre-estimation value in the to-be-modeled time period according to the variation law of the receiver differential code bias, and estimate the satellite differential code bias pre-estimation value in the to-be-modeled time period according to the variation law of the satellite differential code bias.

[0040] The ionospheric model construction module is configured to remove the receiver differential code bias pre-estimation value and the satellite differential code bias pre-estimation value from the GNSS observation data in the to-be-modeled time period according to the receiver differential code bias pre-estimation value and the satellite differential code bias pre-estimation value, and then construct the corresponding ionospheric model in the to-be-modeled time period according to the removed GNSS observation data.

[0041] On the basis of the above-mentioned embodiments, a further embodiment of the application provides an electronic device, the device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the ionospheric modeling method described in the above-mentioned application embodiments when executing the computer program.

[0042] On the basis of the above-mentioned embodiments, a further embodiment of the application provides a storage medium, the storage medium comprising a stored computer program, wherein the device in which the storage medium is located executes the ionospheric modeling method described in the above-mentioned application embodiments when the computer program runs.

[0043] Compared with the prior art, the embodiments of the application have the following beneficial effects:

[0044] The application provides an ionospheric modeling method, which first calculates coupled ionospheric delay and differential code bias information corresponding to each historical GNSS observation data according to a plurality of historical GNSS observation data of a to-be-modeled region in a preset time period; then calculates ionospheric delay corresponding to the historical GNSS observation data according to an ionospheric model corresponding to the historical GNSS observation data, separates the coupled ionospheric delay and differential code bias information, and separates receiver differential code bias and satellite differential code bias in the differential code bias information according to the separated differential code bias information.

[0045] Therefore, the application can overcome the problem that differential code bias and ionospheric delay are coupled and cannot be separated, and can calculate ionospheric delay corresponding to historical GNSS observation data according to an ionospheric model corresponding to the historical GNSS observation data, so as to decouple and separate the coupled ionospheric delay and differential code bias information. According to the separated differential code bias data, differential code bias prediction values in a modeling period can be fitted and estimated; the differential code bias prediction values in the GNSS observation data in the modeling period are removed according to the differential code bias prediction values, to obtain pure GNSS observation data, and an ionospheric model corresponding to the modeling period is constructed, so as to improve the precision of the constructed ionospheric model. BRIEF DESCRIPTION OF DRAWINGS

[0046] FIG. 1 is a flowchart of an ionospheric modeling method according to an embodiment of the application;

[0047] FIG. 2 is a flowchart of an ionospheric model construction according to an embodiment of the application;

[0048] FIG. 3 is a structural diagram of an ionospheric modeling device according to an embodiment of the application. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "include" and "have" and any variations thereof in the specification and claims of the present application and the above description of drawings are intended to cover non-exclusive inclusion.

[0051] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0052] Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase that the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combinable with other embodiments.

[0053] In the description of the embodiments of the present application, the term "and / or" is merely an association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are a "or" relationship.

[0054] In the description of the embodiments of the present application, the term "a plurality of" refers to more than two (including two), and similarly, "a plurality of groups" refers to more than two groups (including two groups), and "a plurality of pieces" refers to more than two pieces (including two pieces).

[0055] In the description of the embodiments of the present application, unless otherwise explicitly specified and limited, the technical terms "mounting", "connecting", "connecting", "fixing" and the like should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanical connection, or it can be electrical connection; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0056] Embodiment one

[0057] Please refer to Fig. 1, which is a flowchart of an ionospheric modeling method provided by an embodiment of the present application, including the following specific steps:

[0058] S1, obtaining a plurality of historical GNSS observation data of the region to be modeled in a preset time period; wherein the historical GNSS observation data includes historical pseudorange observation data and historical carrier observation data of all base station receivers;

[0059] Specifically, the charged particles in the ionosphere change complexly, and the existing technology usually assumes that the ionosphere does not change significantly within a certain time in the model establishment process, and the observation values within 15 minutes, half an hour, one hour or even two hours are fitted into a model. This assumption can achieve better results in weak solar activity or ionosphere calm period, but in strong solar activity years, near local noon or solar / earth magnetic storm period, the ionosphere is more active, and the total electron content fluctuation in a short time can reach dozens of TECU (Total Electron Content Unit), causing the model established by the above method to have large errors, which cannot be applied to actual GNSS operation or other industry production processes.

[0060] The receiver and satellite differential code bias is another factor that restricts the precision of the ionosphere modeling process. Since the differential code bias and the ionosphere delay obtained from the GNSS observation values are coupled and cannot be separated, it is necessary to separate them from the total electron content of the ionosphere in the modeling process by assuming that the differential code bias is constant within a short time (one day), but the premise for this assumption to be true is that the receiver state and the environment are stable. However, in actual production processes, the receiver environment temperature and humidity, space radiation in the environment where the satellite is located, service life of the receiver and satellite, and other factors cannot guarantee the stability of the differential code bias.

[0061] Considering that the above assumption is harsh, the present application obtains a high-frequency ionosphere model by first solving the initial value of the receiver and satellite differential code bias, and then gradually establishing an ionosphere model and estimating the time-varying differential code bias. Please refer to FIG. 2 for the flowchart of the ionosphere model construction of the present application, and the specific implementation steps are as follows:

[0062] Step one: collect all the historical GNSS observation data of the receivers of all base stations in the ionosphere region to be modeled in the previous day, and calculate the center of the region as the modeling reference center position; wherein the historical GNSS observation data includes historical pseudorange observation data and historical carrier observation data of all base station receivers.

[0063] S2, calculate the coupled ionosphere delay and differential code bias information corresponding to each historical GNSS observation data; wherein the differential code bias information includes: receiver differential code bias and satellite differential code bias;

[0064] Preferably, the calculating the coupled ionospheric delay and differential code bias information corresponding to each historical GNSS observation data comprises: calculating a corresponding geometry-free combined pseudo-range observation value according to the historical pseudo-range observation data, and calculating a corresponding geometry-free combined carrier observation value according to the historical carrier observation data; calculating a mean value of a sum of the geometry-free combined pseudo-range observation value and the geometry-free combined carrier observation value in a same preset arc segment, and then calculating the coupled ionospheric delay and differential code bias information corresponding to each historical GNSS observation data according to the mean value and the geometry-free combined carrier observation value.

[0065] Preferably, the coupled ionospheric delay and differential code bias information is:

[0066] wherein, denotes the geometry-free combined carrier observation value in meters, STEC denotes the ionospheric delay, f1 denotes a frequency corresponding to the geometry-free combined pseudo-range observation value, f2 denotes a frequency corresponding to the geometry-free combined carrier observation value, c denotes the speed of light in vacuum, and DCB r,1|2 denotes a receiver differential code bias of the receiver at the frequency f1 and the frequency f2, denotes a satellite differential code bias of the satellite at the frequency f1 and the frequency f2, arc denotes a mean value of a sum of the geometry-free combined pseudo-range observation value and the geometry-free combined carrier observation value in a same preset arc segment.

[0067] Step two: using the historical GNSS observation data collected in step one, the phase-smoothed pseudo-range or the undifferenced non-combined PPP method is used to calculate the ionospheric delay and the differential code bias information corresponding to each historical GNSS observation value.

[0068] In a specific embodiment, the calculation process of the phase-smoothed pseudo-range method is given. Firstly, the geometry-free combined observation values of the pseudo-range and the carrier are obtained by using the historical GNSS observation data:

[0069] wherein, and denote the geometry-free combined pseudo-range observation value and the geometry-free combined carrier observation value in meters, STEC denotes the slant TEC on the GNSS signal path, f1 and f2 denote frequencies corresponding to two observation values participating in the calculation, c is the speed of light in vacuum, and DCB r,1|2 and denote the differential code biases of the receiver and the satellite at the frequency f1 and the frequency f2, respectively, r,f and denote the carrier hardware delays of the receiver and the satellite, respectively, and λ fdenotes the carrier wavelength with frequency f, denotes the ambiguity number of the carrier observation, t denotes the observation time.

[0070] in formula (1) b r,f and The assumption is stable and unchanged within an observation arc segment, so after removing the cycle slip, the average of the observations within the same arc segment can be obtained as follows:

[0071] where n is the number of observations within a continuous arc segment, <P4+φ4> arc denotes the average of the sum of the geometric-free combined pseudorange observation and the geometric-free combined carrier observation within a continuous arc segment, and after obtaining this average, formula (1) can be obtained:

[0072] Further, the STEC (as ionospheric delay) can be obtained by phase-smoothing the pseudorange observation:

[0073] The above formula (4) is the coupled ionospheric delay and differential code bias information.

[0074] S3, according to the ionospheric model corresponding to the historical GNSS observation data, calculating the ionospheric delay corresponding to the historical GNSS observation data, and then separating the coupled ionospheric delay and differential code bias information;

[0075] Preferably, according to the ionospheric model corresponding to the historical GNSS observation data, calculating the ionospheric delay corresponding to the historical GNSS observation data, and then separating the coupled ionospheric delay and differential code bias information, comprising: judging whether the historical GNSS observation data exists corresponding ionospheric model, when it is determined that the historical GNSS observation data exists corresponding ionospheric model, according to the ionospheric model corresponding to the historical GNSS observation data, calculating the piercing point coordinates of the historical GNSS observation data at the height of the corresponding ionospheric model; according to the piercing point coordinates, calculating the vertical path value corresponding to the historical GNSS observation data; calculating the ionospheric projection function value corresponding to the historical GNSS observation data, and then converting the vertical path value to the corresponding slant path value according to the ionospheric projection function value; taking the slant path value as the ionospheric delay corresponding to the historical GNSS observation data, and then separating the coupled ionospheric delay and differential code bias information.

[0076] Preferably, the ionosphere model corresponding to the historical GNSS observation data is used to calculate the ionosphere delay corresponding to the historical GNSS observation data, and then the coupled ionosphere delay and differential code bias information is separated, and the method further comprises: when it is determined that the historical GNSS observation data does not have a corresponding ionosphere model, it is assumed that the receiver differential code bias and the satellite differential code bias corresponding to the historical GNSS observation data are the same in a preset time period, and then the ionosphere model corresponding to the historical GNSS observation data is solved according to a preset ionosphere model and a differential code bias solving equation; the piercing point coordinates of the historical GNSS observation data on the height of the corresponding ionosphere model are calculated according to the ionosphere model corresponding to the historical GNSS observation data; the vertical path value corresponding to the historical GNSS observation data is calculated according to the piercing point coordinates; the ionosphere projection function value corresponding to the historical GNSS observation data is calculated, and then the vertical path value is converted into a corresponding slant path value according to the ionosphere projection function value; the slant path value is taken as the ionosphere delay corresponding to the historical GNSS observation data, and then the coupled ionosphere delay and differential code bias information is separated.

[0077] S4, separating the receiver differential code bias and the satellite differential code bias in the differential code bias information according to the separated differential code bias information;

[0078] Preferably, the step of separating the receiver differential code bias and the satellite differential code bias in the differential code bias information according to the separated differential code bias information comprises: according to the separated differential code bias information, setting the sum of the satellite differential code biases in the differential code bias information to zero, solving the corresponding receiver differential code bias, and then separating the receiver differential code bias and the satellite differential code bias in the differential code bias information.

[0079] Step three: if the existing ionosphere model product at the time corresponding to the historical GNSS observation value of the modeling area can be obtained, the product can be used to separate the differential code bias of the receiver and the satellite from the ionosphere delay, and obtain the time-varying differential code bias of the receiver and the satellite. Using the existing ionosphere model product as a known value, the ionosphere delay value is subtracted from the phase smoothing pseudo-range observation value obtained in step two, and the combined value of the receiver and satellite differential code bias can be obtained.

[0080] The two are separated by the zero mean assumption. Considering the time-varying characteristics of the two, the receiver and satellite differential code bias of each epoch are estimated. Since the differential code bias changes slowly, the last epoch receiver and satellite differential code bias modified value is used as a constraint for Kalman filter estimation to control the change rate. After a period of time, stable Kalman filter output results can be obtained. The specific implementation process is as follows:

[0081] 1、Calculate the ionosphere model results corresponding to all historical GNSS observations at the same epoch, and get the vertical path TEC corresponding to the historical GNSS observations, denoted as Where s represents the satellite number, r represents the receiver number, and t represents the current epoch.

[0082] First, calculate the piercing point coordinates of the historical GNSS observations on the ionosphere model height: z=π / 2-El α=z-z'

[0083] In the formula: A is the satellite azimuth angle, El is the satellite elevation angle, R E is the Earth's radius (6378km), H is the piercing point height, which is set to 506.7km here, φ and λ are the latitude and longitude of the station, and φ IPP and λ IPP are the latitude and longitude of the piercing point. Then calculate the vertical path using the ionosphere model, which varies according to the model.

[0084] 2、Calculate the ionosphere projection function value MF corresponding to the historical GNSS observations, and convert the vertical path model value (t,φ IPP ,λ IPP ) in step 1 to the slant path model value

[0085] In the formula, R is the Earth's radius, H0=506.7KM, and a=0.9782.

[0086] 3、Based on formula (3) in step two, remove the ionosphere STEC represented by from the right side of the equation to get the combined value of DCB r,1|2 and , and let the sum of all satellite be zero, separate DCB r,1|2 and , and calculate as follows:

[0087] Where L represents the combined value of DCB r,1|2 and calculated using formula (3) and formula (6) in step two, H is the design matrix, the last row represents the zero mean constraint, X is the current epoch DCB r,1|2 (t) and value to be solved, where the order in X is DCB before DCB r,1|2 (t).

[0088] 4. Calculate the time-varying receiver DCB and satellite DCB values sequentially for each epoch using Kalman filter.

[0089] Step four: If the ionospheric model product corresponding to the epoch cannot be obtained in step three, first assume that the receiver DCB and satellite DCB are the same within a day, and use the least squares method to solve the ionospheric model parameters and the DCB together, so as to separate the two.

[0090] Then use the obtained ionospheric model to solve the time-varying receiver DCB and satellite DCB using the method in step three. Use the phase smoothing pseudorange observation value obtained by formula (3) in step two to establish the solving equation of the ionospheric model and the DCB, which has a set of ionospheric model parameters for each period and uses the same DCB parameter for each satellite and receiver within a day:

[0091] Where, ion i (t n ) represents the i th ionospheric model parameter in the t n period. Through the above method, the ionospheric model and the DCB r,1|2 and To obtain the time-varying DCB r,1|2 and , the ionospheric model in the above formula is brought into step three, and the time-varying DCB r,1|2 (t) and

[0092] S5, according to the separated receiver DCB, the change rule of the receiver DCB is fitted, and according to the separated satellite DCB, the change rule of the satellite DCB is fitted;

[0093] Step five: Analyze the time-varying receiver DCB and satellite DCB obtained in step three or step four, and use mathematical model fitting or time-frequency analysis method to find the change rule of each receiver DCB and satellite DCB. Since the receiver DCB is mainly affected by temperature, and the satellite DCB is mainly affected by equipment aging, a simple polynomial can be used for fitting, and the fitting result is:

[0094] Where, A i represents the polynomial coefficient, n represents the polynomial order, t represents the local time (in seconds), and the receiver DCB data collected historically is fitted by the least squares method.

[0095] S6, estimating the receiver differential code bias pre-estimate value in the modeling time period according to the change rule of the receiver differential code bias, and estimating the satellite differential code bias pre-estimate value in the modeling time period according to the change rule of the satellite differential code bias;

[0096] Step six: using the change rule obtained in step five, estimating the receiver differential code bias pre-estimate value in the modeling time period according to the change rule of the receiver differential code bias, and estimating the satellite differential code bias pre-estimate value in the modeling time period according to the change rule of the satellite differential code bias.

[0097] S7, according to the receiver differential code bias pre-estimate value and the satellite differential code bias pre-estimate value, eliminating the receiver differential code bias pre-estimate value and the satellite differential code bias pre-estimate value in the GNSS observation data in the modeling time period, and then constructing the ionospheric model corresponding to the modeling time period according to the GNSS observation data after elimination.

[0098] Preferably, the ionospheric modeling method further comprises: eliminating the ionospheric delay in the GNSS observation data according to the constructed ionospheric model and the corresponding GNSS observation data, and then performing GNSS positioning according to the GNSS observation data after elimination.

[0099] Step seven: eliminating the receiver differential code bias pre-estimate value and the satellite differential code bias pre-estimate value from the GNSS observation value in the modeling time period, and then estimating the pure ionospheric delay information for modeling, wherein the Kalman filtering method is used in the modeling process to estimate the ionospheric delay model parameters and the receiver and satellite differential code bias.

[0100] The data obtained in steps one to five is integrated to calculate the ionospheric model parameters in the ultra-high frequency modeling time period (1s), then the initial values of the receiver and satellite differential code bias in the time period are estimated using formula (9), the ionospheric delay value is obtained by eliminating the differential code bias using formula (3), and the equations in formula (8) and the Kalman filtering method are used to solve the ionospheric model, the DCB r,1|2 (t) and , finally the solving results are added to the differential code bias rule estimation process in step five, and steps five and six are repeated. Finally, the ionospheric delay in the GNSS observation data is eliminated according to the constructed ionospheric model and the corresponding GNSS observation data, and then GNSS positioning is performed according to the GNSS observation data after elimination.

[0101] Embodiment two

[0102] Please refer to Figure 3, it is a kind of ionospheric modeling device structural schematic diagram provided by an embodiment of the application, the device includes: historical observation data acquisition module, coupling data calculation, coupling data separation module, difference code bias information separation module, variation law fitting module, difference code bias estimation module and ionospheric model construction module;

[0103] The historical observation data acquisition module is used to acquire a plurality of historical GNSS observation data of a region to be modeled in a preset time period;Wherein, the historical GNSS observation data includes: historical pseudorange observation data and historical carrier observation data of all base station receivers;

[0104] The coupling data calculation module is used to calculate the coupled ionospheric delay and difference code bias information corresponding to each historical GNSS observation data;Wherein, the difference code bias information includes: receiver difference code bias and satellite difference code bias;

[0105] The coupling data separation module is used to calculate the ionospheric delay corresponding to the historical GNSS observation data according to the ionospheric model corresponding to the historical GNSS observation data, and then separate the coupled ionospheric delay and difference code bias information;

[0106] The difference code bias information separation module is used to separate the receiver difference code bias and satellite difference code bias in the difference code bias information according to the separated difference code bias information;

[0107] The variation law fitting module is used to fit the variation law of the receiver difference code bias according to the separated receiver difference code bias, and fit the variation law of the satellite difference code bias according to the separated satellite difference code bias;

[0108] The difference code bias estimation module is used to estimate the receiver difference code bias estimate value in the time period to be modeled according to the variation law of the receiver difference code bias, and estimate the satellite difference code bias estimate value in the time period to be modeled according to the variation law of the satellite difference code bias;

[0109] The ionospheric model construction module is used to remove the receiver difference code bias estimate value and the satellite difference code bias estimate value in the GNSS observation data in the time period to be modeled according to the receiver difference code bias estimate value and the satellite difference code bias estimate value, and then construct the corresponding ionospheric model in the time period to be modeled according to the GNSS observation data after removal.

[0110] It should be noted that the apparatus embodiments described above are only illustrative, and the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e. can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. In addition, the connection between the modules in the apparatus embodiment provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity, the specific working process of the apparatus described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0112] Embodiment three

[0113] Correspondingly, the embodiment of the present application provides an electronic device, the device includes a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, when the processor executes the computer program, the ionospheric modeling method described in the above embodiment of the application is realized.

[0114] The electronic device can be a desktop computer, a notebook computer, a palm computer and a cloud server, etc. The device can include but is not limited to a processor and a memory.

[0115] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The processor is the control center of the device, and connects various parts of the device through various interfaces and lines.

[0116] Embodiment four

[0117] Correspondingly, the embodiment of the present application provides a storage medium, the storage medium comprising a stored computer program, wherein the computer program controls a device where the storage medium is located to perform the ionospheric modeling method when the computer program is running.

[0118] The memory can be configured to store the computer program, and the processor can realize various functions of the device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function, and the like; and the data storage area can store data created according to use of the mobile phone and the like. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0119] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. The computer program can realize the steps of each method embodiment when executed by the processor. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the computer-readable medium can include or exclude contents according to requirements of legislation and patent practice in a jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include an electrical carrier signal and a telecommunication signal.

[0120] The above describes the preferred embodiments of the present application. It should be noted that, for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements are also considered to be within the protection scope of the present application.

Claims

1. A method of ionospheric modeling, characterized by, The method comprises the following steps: acquiring a plurality of historical GNSS observation data of a region to be modeled within a preset time period; wherein the historical GNSS observation data comprises historical pseudorange observation data and historical carrier observation data of all base station receivers; calculating coupled ionospheric delay and differential code bias information corresponding to each historical GNSS observation data; wherein the differential code bias information comprises receiver differential code bias and satellite differential code bias; calculating ionospheric delay corresponding to the historical GNSS observation data according to an ionospheric model corresponding to the historical GNSS observation data, and then separating the coupled ionospheric delay and differential code bias information; separating the receiver differential code bias and the satellite differential code bias in the differential code bias information according to the separated differential code bias information; fitting the variation law of the receiver differential code bias according to the separated receiver differential code bias, and fitting the variation law of the satellite differential code bias according to the separated satellite differential code bias; estimating the receiver differential code bias prediction value within the modeling period according to the variation law of the receiver differential code bias, and estimating the satellite differential code bias prediction value within the modeling period according to the variation law of the satellite differential code bias; eliminating the receiver differential code bias prediction value and the satellite differential code bias prediction value in the GNSS observation data within the modeling period according to the receiver differential code bias prediction value and the satellite differential code bias prediction value, and then constructing the corresponding ionospheric model within the modeling period according to the eliminated GNSS observation data.

2. The ionospheric modeling method of claim 1, wherein, The method further comprises the following steps: eliminating the ionospheric delay in the GNSS observation data according to the constructed ionospheric model and the corresponding GNSS observation data, and then performing GNSS positioning according to the eliminated GNSS observation data.

3. The ionospheric modeling method of claim 1, wherein, The calculation of the coupled ionospheric delay and differential code bias information corresponding to each historical GNSS observation data comprises the following steps: calculating the corresponding non-geometric combined pseudorange observation value according to the historical pseudorange observation data, and calculating the corresponding non-geometric combined carrier observation value according to the historical carrier observation data; calculating the average of the sum of the non-geometric combined pseudorange observation value and the non-geometric combined carrier observation value within the same preset arc segment, and then calculating the coupled ionospheric delay and differential code bias information corresponding to each historical GNSS observation data according to the average and the non-geometric combined carrier observation value.

4. The ionospheric modeling method of claim 3, wherein, The coupled ionospheric delay and differential code bias information is: wherein, denotes the ionospheric delay, f1 denotes the frequency corresponding to the geometry-free combined pseudorange observation, f2 denotes the frequency corresponding to the geometry-free combined carrier observation, c denotes the speed of light in vacuum, DCB r,1|2 denotes the receiver differential code bias of the receiver at frequency f1 and frequency f2, denotes the satellite differential code bias of the satellite at frequencies f1 and f2, <P4+ φ4> arc denotes the mean value of the sum of the geometric-free combined pseudorange observation and the geometric-free combined carrier observation within the same preset arc segment.

5. The ionospheric modeling method of claim 1, wherein, The calculation of the ionospheric delay corresponding to the historical GNSS observation data according to the ionospheric model corresponding to the historical GNSS observation data, and then the separation of the coupled ionospheric delay and differential code bias information comprises the following steps: determining whether the historical GNSS observation data has a corresponding ionospheric model, and when it is determined that the historical GNSS observation data has a corresponding ionospheric model, calculating the piercing point coordinates of the historical GNSS observation data at the height of the corresponding ionospheric model according to the ionospheric model corresponding to the historical GNSS observation data; calculating the vertical path value corresponding to the historical GNSS observation data according to the piercing point coordinates; calculating ionospheric projection function values corresponding to the historical GNSS observation data, and then converting the vertical path values into corresponding slant path values according to the ionospheric projection function values; taking the slant path values as ionospheric delays corresponding to the historical GNSS observation data, and then separating coupled ionospheric delays and differential code bias information.

6. The ionospheric modeling method of claim 5, wherein, The method further includes: when it is determined that the historical GNSS observation data does not have a corresponding ionospheric model, assuming that receiver differential code biases and satellite differential code biases corresponding to the historical GNSS observation data are the same within a preset time period, and then solving the ionospheric model corresponding to the historical GNSS observation data according to a preset solving equation of the ionospheric model and the differential code bias; calculating piercing point coordinates of the historical GNSS observation data at a height of the corresponding ionospheric model according to the ionospheric model corresponding to the historical GNSS observation data; calculating vertical path values corresponding to the historical GNSS observation data according to the piercing point coordinates; calculating ionospheric projection function values corresponding to the historical GNSS observation data, and then converting the vertical path values into corresponding slant path values according to the ionospheric projection function values; taking the slant path values as ionospheric delays corresponding to the historical GNSS observation data, and then separating coupled ionospheric delays and differential code bias information.

7. The ionospheric modeling method of claim 1, wherein, The method further includes: separating receiver differential code biases and satellite differential code biases in the differential code bias information according to the separated differential code bias information.

8. An ionospheric modeling apparatus characterized by comprising: The method further includes: assuming that a sum of the satellite differential code biases in the differential code bias information is zero, solving corresponding receiver differential code biases, and then separating the receiver differential code biases and the satellite differential code biases in the differential code bias information. The method further includes: a historical observation data acquisition module, a coupled data calculation module, a coupled data separation module, a differential code bias information separation module, a change law fitting module, a differential code bias estimation module, and an ionospheric model construction module; The historical observation data acquisition module is configured to acquire a plurality of historical GNSS observation data of a preset time period in a region to be modeled, wherein the historical GNSS observation data includes historical pseudorange observation data and historical carrier observation data of all base station receivers. The coupled data calculation module is configured to calculate coupled ionospheric delays and differential code bias information corresponding to each historical GNSS observation data, wherein the differential code bias information includes receiver differential code biases and satellite differential code biases. The coupled data separation module is configured to calculate ionospheric delays corresponding to the historical GNSS observation data according to an ionospheric model corresponding to the historical GNSS observation data, and then separate coupled ionospheric delays and differential code bias information. The differential code bias information separation module is configured to separate the receiver differential code bias and the satellite differential code bias in the differential code bias information according to the separated differential code bias information. The variation law fitting module is configured to fit a variation law of the receiver differential code bias according to the separated receiver differential code bias, and fit a variation law of the satellite differential code bias according to the separated satellite differential code bias. The differential code bias estimation module is configured to estimate a receiver differential code bias prediction value in a modeling period to be modeled according to the variation law of the receiver differential code bias, and estimate a satellite differential code bias prediction value in the modeling period to be modeled according to the variation law of the satellite differential code bias. The ionospheric model construction module is configured to remove the receiver differential code bias prediction value and the satellite differential code bias prediction value from GNSS observation data in the modeling period to be modeled according to the receiver differential code bias prediction value and the satellite differential code bias prediction value, and then construct a corresponding ionospheric model in the modeling period to be modeled according to the removed GNSS observation data.

9. An electronic device, comprising: The storage medium includes a stored computer program, wherein the computer program controls a device where the storage medium is located to perform the ionospheric modeling method according to any one of claims 1 to 7 when the computer program is running.

10. A storage medium, characterized by The storage medium includes a stored computer program, wherein the computer program controls a device where the storage medium is located to perform the ionospheric modeling method according to any one of claims 1 to 7 when the computer program is running.

Images