A method, apparatus, electronic device, and storage medium for ionospheric model localization based on IRI model correction.

Abstract

This invention discloses a positioning method, apparatus, electronic device, and storage medium based on an ionospheric model modified by an IRI model. The method includes: acquiring satellite observation values; calculating and generating a measured oblique ionospheric delay error value for each satellite observation value; generating a measured ionospheric oblique delay model and a first model accuracy of the measured ionospheric oblique delay model based on the measured oblique ionospheric delay error value and the satellite observation values; generating an error model value using an IRI model by combining historical trajectories and predicted trajectories; correcting the error model value for the predicted time period based on the measured oblique ionospheric delay error value and the error model value for historical time periods; generating a predicted oblique ionospheric delay model and a second model accuracy based on the corrected model value; and sending the measured ionospheric oblique delay model, the first model accuracy, the predicted oblique ionospheric delay model, and the second model accuracy to a user terminal for positioning. Implementing this invention can improve positioning accuracy.

Description

Technical Field

[0001] This invention relates to the field of ionospheric delay modeling technology, and specifically to a method, apparatus, electronic device, and storage medium for locating an ionospheric model based on IRI model correction. Background Technology

[0002] In Global Navigation Satellite System (GNSS) positioning, ionospheric delay error is a key factor affecting the accuracy and convergence time of Single Point Positioning (SPP), Precise Single Point Positioning (PPP), and the widely used PPP-RTK positioning technology in recent years. Therefore, providing an accurate and reliable estimate of ionospheric delay error is crucial for achieving high-precision positioning, in order to improve positioning accuracy and accelerate positioning convergence speed.

[0003] However, we are currently in the active phase of the 25th solar cycle, and charged particles in the ionosphere are more active due to high-energy solar radiation, exhibiting complex ionospheric variations. Therefore, traditional methods using simple polynomial models for regional ionospheric modeling lack sufficient accuracy to meet the needs of high-precision positioning. Existing technologies, such as the ionospheric oblique delay model, which uses data from base station observations to model the ionosphere, can avoid projection function errors and provide relatively high-precision delay corrections within a small range. However, due to the high-speed motion of satellites, the reference point becomes far from the actual observation value over time, resulting in a short validity period requiring frequent updates. Furthermore, its application is limited by network transmission delays and data loss. Summary of the Invention

[0004] This invention provides a positioning method, apparatus, electronic device, and storage medium based on an ionospheric model modified by an IRI model. By implementing this invention, the validity period of the ionospheric slant delay model can be increased, providing users with a more stable and persistent ionospheric slant delay model for global navigation satellite system positioning, thereby improving positioning accuracy.

[0005] One embodiment of the present invention provides a method for locating an ionospheric model based on an IRI model correction, comprising:

[0006] Acquire several satellite observations for each satellite during historical periods, motion trajectory data for each satellite during historical periods, motion trajectory data for each satellite during predicted periods, pseudorange hardware delay for each reference station, pseudorange hardware delay for each satellite, geographical location information for each reference station, and IRI model.

[0007] For each satellite observation, the corresponding measured oblique ionospheric delay error value is calculated and generated based on the pseudorange hardware delay of the corresponding reference station and the pseudorange hardware delay of the corresponding satellite.

[0008] For each satellite, based on the corresponding satellite observations, the corresponding measured oblique ionospheric delay error value, and the geographical location information of the corresponding reference station, a measured ionospheric oblique delay model and the first model accuracy of the measured ionospheric oblique delay model are generated.

[0009] Based on the satellite motion trajectory data for the corresponding historical time period and the satellite motion trajectory data for the corresponding predicted time period, a first error model value for the historical time period and a second error model value for the predicted time period are generated using the IRI model. The measured oblique ionospheric delay error value is matched and grouped with the first error model value. For each group, the corresponding systematic deviation and standard deviation are calculated based on the corresponding measured oblique ionospheric delay error value and the first error model value. For each second error model value, it is corrected according to the corresponding systematic deviation to obtain the corresponding oblique ionospheric delay error model value, which is used as the corresponding third error model value.

[0010] For each satellite, based on the satellite's motion trajectory data for the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation, and the corresponding third error model value, a corresponding oblique ionospheric delay model and a second model accuracy of the oblique ionospheric delay model are generated.

[0011] Each measured ionospheric oblique delay model, each first model accuracy, each predicted ionospheric oblique delay model, and each second model accuracy are sent to the user terminal for positioning purposes.

[0012] Furthermore, the step of generating a corresponding measured ionospheric oblique delay model and a first model accuracy of the measured ionospheric oblique delay model based on the corresponding satellite observations, the corresponding measured oblique ionospheric delay error value, and the geographical location information of the corresponding reference station includes:

[0013] For each satellite observation, the location of the corresponding puncture point is calculated using the ionospheric single-layer thin shell assumption, based on the geographical location information of the corresponding reference station.

[0014] The average value of the puncture point locations is used to generate the center coordinates of the satellite observation values;

[0015] Based on the center coordinates of the observed values, the puncture point position corresponding to each satellite observation value, and the measured oblique ionospheric delay error value corresponding to each satellite observation value, the measured oblique ionospheric delay model of the satellite is generated by fitting using the least squares method.

[0016] Based on the measured oblique ionospheric delay error value corresponding to each satellite observation, the residual statistical value of the measured oblique ionospheric delay model is calculated as the first model accuracy of the measured oblique ionospheric delay model.

[0017] Furthermore, the step of generating a first error model value for the historical period and a second error model value for the predicted period based on the satellite's motion trajectory data for the corresponding historical period and the satellite's motion trajectory data for the corresponding predicted period using the IRI model includes:

[0018] Based on the satellite's motion trajectory data for the historical period, a first projection function is calculated and generated, and the first error model value corresponding to the historical period is calculated and generated using the IRI model.

[0019] Based on the satellite's motion trajectory data for the predicted time period, a second projection function is calculated and generated. Using the IRI model, a second error model value corresponding to the predicted time period is calculated and generated.

[0020] Furthermore, the step of matching and grouping the measured oblique ionospheric delay error value with the first error model value includes:

[0021] By combining the measured oblique ionospheric delay error values ​​consistent with the reference station, satellite, and epoch with the values ​​of the first error model, several matching sets are obtained;

[0022] The matching sets that are the same for the base station and the satellite are grouped to obtain several matching groups.

[0023] Furthermore, the systematic deviation and standard deviation are calculated using the following formula:

[0024]

[0025] in, The difference between the measured oblique ionospheric delay error value of the reference station r at epoch t for satellite s and the corresponding first error model value; This represents the measured oblique ionospheric delay error value; This is the value of the first error model; The systematic deviation of the base station r from the satellite s; T is the total number of epochs in the historical modeling period; Let be the standard deviation of the base station r relative to the satellite s.

[0026] Furthermore, for each second error model value, the corresponding systematic deviation is corrected using the following formula to obtain the corresponding oblique ionospheric delay error model value:

[0027]

[0028] in, The value of the oblique ionospheric delay error model for the reference station r relative to the satellite s; The second error model value for satellite s is given by reference station r at epoch T0; T0 is the time when modeling was performed. The systematic deviation of the reference station r from the satellite s; t predict To predict the coverage time of the model.

[0029] Furthermore, the step of generating a corresponding predictive oblique ionospheric delay model and a second model accuracy of the predictive oblique ionospheric delay model based on the satellite's motion trajectory data for the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation, and the corresponding third error model value includes:

[0030] For each predicted period, the location of the puncture point is calculated using the assumption of a single-layer thin shell of the ionosphere, based on the geographical location information of the corresponding reference station and the satellite's motion trajectory data for each period.

[0031] The average value of the corresponding puncture point locations is taken to generate the center coordinates of the satellite observation values.

[0032] Based on the center coordinates of the observed values, the puncture point position corresponding to the satellite's motion trajectory data for each prediction period, and the third error model value corresponding to the satellite's motion trajectory data for each prediction period, a satellite prediction oblique ionospheric delay model is generated by fitting using the least squares method.

[0033] The average of the standard deviations corresponding to the satellites is used as the second model accuracy of the corresponding predictive oblique ionospheric delay model.

[0034] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0035] An embodiment of the present invention provides a positioning device for an ionospheric model based on IRI model correction, comprising: a data acquisition module, a measured oblique ionospheric delay error value generation module, a measured model generation module, a model value correction module, a prediction model generation module, and a model transmission module.

[0036] The data acquisition module is used to acquire several satellite observations of each satellite during a historical period, motion trajectory data of each satellite during a historical period, motion trajectory data of each satellite during a predicted period, pseudorange hardware delay of each reference station, pseudorange hardware delay of each satellite, geographical location information of each reference station, and IRI model.

[0037] The measured oblique ionospheric delay error value generation module is used to calculate and generate the corresponding measured oblique ionospheric delay error value for each satellite observation value based on the pseudorange hardware delay of the corresponding reference station and the pseudorange hardware delay of the corresponding satellite.

[0038] The measured model generation module is used to generate a corresponding measured ionospheric oblique delay model and a first model accuracy of the measured ionospheric oblique delay model for each satellite, based on the corresponding satellite observation value, the corresponding measured oblique ionospheric delay error value and the geographical location information of the corresponding reference station.

[0039] The model value correction module is used to generate a first error model value for the historical period and a second error model value for the predicted period based on the satellite motion trajectory data for the corresponding historical period and the satellite motion trajectory data for the corresponding predicted period using an IRI model; match and group the measured oblique ionospheric delay error value with the first error model value; for each group, calculate and generate the corresponding systematic deviation and standard deviation based on the corresponding measured oblique ionospheric delay error value and the first error model value; and correct each second error model value based on the corresponding systematic deviation to obtain the corresponding oblique ionospheric delay error model value, which serves as the corresponding third error model value.

[0040] The prediction model generation module is used to generate a corresponding prediction oblique ionospheric delay model and a second model accuracy of the prediction oblique ionospheric delay model for each satellite, based on the satellite's motion trajectory data for the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation, and the corresponding third error model value.

[0041] The model transmission module is used to send each measured ionospheric oblique delay model, each first model accuracy, each predicted ionospheric oblique delay model, and each second model accuracy to the user terminal for positioning purposes.

[0042] Based on the above method embodiments, the present invention provides corresponding electronic device embodiments.

[0043] An embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it can implement the ionospheric model localization method based on IRI model correction as described in any of the above-described method embodiments.

[0044] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments.

[0045] One embodiment of the present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, can implement the ionospheric model localization method based on IRI model correction as described in any of the above-described method embodiments.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] This invention provides a positioning method, apparatus, electronic device, and storage medium based on an ionospheric model modified by an IRI model. The method calculates the measured oblique ionospheric delay error value for each satellite observation based on the pseudorange hardware delay of a reference station and the pseudorange hardware delay of the satellite. Then, based on the measured oblique ionospheric delay error value and the geographical location of the reference station, a measured ionospheric oblique delay model and a corresponding first model accuracy are generated. Combining historical and predicted trajectories, the IRI model is used to generate first error model values ​​for historical time periods and second error model values ​​for predicted time periods. The second error model value is modified based on the measured oblique ionospheric delay error value and the first error model value. A predicted oblique ionospheric delay model and a corresponding second model accuracy are generated based on the modified model value. The measured ionospheric oblique delay model, the first model accuracy, the predicted oblique ionospheric delay model, and the second model accuracy are sent to the user terminal for positioning purposes.

[0048] This invention calculates the measured oblique ionospheric delay error using actual satellite observations from a reference station and constructs a measured ionospheric oblique delay model. Based on the measured oblique ionospheric delay error value with correction, a predictive model is established by using an IRI model (a physical model) to make short-term predictions of the ionospheric delay error on the subsequent satellite trajectory. Both models are simultaneously broadcast to the user terminal. When the user cannot update the ionospheric oblique delay model in a timely manner, the predictive model can be used to correct the ionospheric delay error during the positioning process, ensuring that the positioning results are in a high-precision and usable state for the vast majority of the time. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating a method for locating an ionospheric model based on an IRI model correction, provided in an embodiment of the present invention.

[0050] Figure 2 This is a flowchart illustrating a method for locating an ionospheric model based on an IRI model correction, according to another embodiment of the present invention.

[0051] Figure 3 This is a schematic diagram of the structure of a positioning device based on an IRI model correction according to an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] like Figure 1 As shown, an embodiment of the present invention provides a method for locating an ionospheric model based on an IRI model correction, comprising at least the following steps:

[0054] Step S1: Obtain several satellite observations for each satellite during the historical period, motion trajectory data for each satellite during the historical period, motion trajectory data for each satellite during the predicted period, pseudorange hardware delay for each reference station, pseudorange hardware delay for each satellite, geographical location information for each reference station, and IRI model.

[0055] Preferably, the pseudorange hardware delay of the base station and the pseudorange hardware delay of the satellite are defined as follows:

[0056] DCB r，1|2 =B r，1 -B r，2 (1)

[0057]

[0058] Among them, DCBR r，1|2 The pseudorange hardware delay at frequencies f1 and f2 of the reference station; B represents the pseudorange hardware delay of the satellite at frequencies f1 and f2. r，1 B is the code delay at frequency f1 of the reference station; r，2 The code delay at frequency f2 is the reference station's value. The code delay of the satellite at frequency f1; The code delay of the satellite at frequency f2;

[0059] The acquired IRI model includes the necessary input data, including CCIR, URSI, DGRF, IGRF, MCSAT, IG index, AP index, and F107 index. All of the aforementioned necessary input data can be downloaded from the official International Reference Ionosphere data server. CCIR, URSI, and MCSAT data must cover the period from 2011 to 2022; DGRF data requires data at 5-year intervals from 1945 to 2015; and IGRF data must be for the year 2020. The IG, AP, and F107 indices are updated monthly and need to be updated promptly.

[0060] Specifically, the pseudorange hardware delay of the base station and the pseudorange hardware delay of the satellite are downloaded from the GNSS service provider or estimated by the provider.

[0061] Step S2: For each satellite observation, calculate and generate the corresponding measured oblique ionospheric delay error value based on the pseudorange hardware delay of the corresponding reference station and the pseudorange hardware delay of the corresponding satellite.

[0062] In a preferred embodiment, the measured oblique ionospheric delay error value can be calculated and generated using a phase smoothing pseudorange method, the specific steps of which include:

[0063] The pseudorange and carrier observations are obtained by geometrically-free combination of the GNSS observation equations; the GNSS observation equations are as follows:

[0064]

[0065] Where E[·] is the expectation operator, r is the base station identifier, s is the satellite identifier, and t is the epoch time. The pseudorange observation value from the reference station r to the satellite s at time t. Let λ be the carrier observation value from reference station r to satellite s at time t. s Let be the carrier wavelength of the observation value at the i-th frequency point of satellite s. Let dt be the distance from the reference station r to the satellite s at time t. r (t) represents the receiver clock error at time t, and dt s (t) represents the satellite clock difference at time t, Trop r (t) represents the tropospheric delay of base station r at time t. Let ft be the ionospheric delay from reference station r to satellite s at time t, and f0 be the reference frequency (using 1572.42MHz). i Let b be the frequency of the observation at the i-th frequency point. r，i (t) represents the carrier hardware delay at the i-th frequency point of the reference station r at time t. Let N be the carrier hardware delay at frequency i of satellite s at time t. i Let be the carrier ambiguity of the observation at frequency point i.

[0066] The obtained pseudorange and carrier non-geometric observations are as follows:

[0067]

[0068] in, These are pseudorange observations without geometric combination; For observations without geometrically combined carrier waves, r is the identifier of the base station; s is the identifier of the satellite; λ4 represents the wavelength without geometrically combined carrier waves; STEC represents the total electron content (Slant TEC) along the GNSS signal path; f1 and f2 represent the frequencies corresponding to the two observations used in the calculation; c is the speed of light in vacuum; DCB r,1|2 This refers to the differential code offset of the receiver at frequencies f1 and f2. b represents the differential code offset of the satellite at frequencies f1 and f2. r,f The carrier hardware delay of the receiver corresponding to frequency point f; λ is the carrier hardware delay of the satellite corresponding to frequency point f. f Let f be the carrier wavelength. The ambiguity cycle number of the carrier observation.

[0069] Ambiguity cycles of carrier observations The carrier hardware delay b of the receiver corresponding to frequency point f r，f The carrier hardware delay of the satellite corresponding to frequency point f The pseudorange is stable and invariant when there are no cycle slips in a short period of time. Therefore, after removing cycle slips, the average of the short-term pseudorange and the geometrically unobserved carrier can be used to obtain the following relationship:

[0070]

[0071] in, It represents the mean of the sum of pseudorange observations without geometric combination and carrier observations without geometric combination within a continuous arc segment.

[0072] Solving equations (5) and (4) simultaneously generates phase-smoothed pseudorange observations corresponding to the satellite observations:

[0073]

[0074] in, These are phase-smoothed pseudorange observations.

[0075] Solving equations (6) and (4) simultaneously, based on the pseudorange hardware delay of the corresponding reference station and the pseudorange hardware delay of the satellite, generates the measured oblique ionospheric delay error value corresponding to the satellite observation:

[0076]

[0077] in, This represents the measured oblique ionospheric delay error value.

[0078] Optionally, the measured oblique ionospheric delay error value can also be calculated and generated using the non-difference non-combination PPP method.

[0079] Step S3: For each satellite, based on the corresponding satellite observations, the corresponding measured oblique ionospheric delay error value, and the geographical location information of the corresponding reference station, generate the measured ionospheric oblique delay model and the first model accuracy of the measured ionospheric oblique delay model.

[0080] In a preferred embodiment, the generation of the measured ionospheric oblique delay model and the first model accuracy of the measured ionospheric oblique delay model based on the corresponding satellite observations, the corresponding measured oblique ionospheric delay error value, and the geographical location information of the corresponding reference station includes:

[0081] For each corresponding satellite observation, the location of the corresponding puncture point is calculated using the ionospheric single-layer thin shell assumption based on the geographical location information of the corresponding reference station.

[0082] In practice, the corresponding puncture point location is calculated using the following formula:

[0083]

[0084] Where z is the zenith distance of the satellite at the reference station, A is the satellite azimuth angle, E1 is the satellite elevation angle, and R... E Where is the Earth's radius, and H is the height of the puncture point. λ represents the latitude and longitude of the base station. and λ IPP These are the latitude and longitude of the puncture point. The satellite azimuth and elevation angles are calculated using the satellite position and the base station position.

[0085] The average value of the puncture point locations is used to generate the center coordinates of the satellite observation values;

[0086] In practice, the satellite's observation center coordinates are generated using the following formula:

[0087]

[0088] Where T represents the total number of epochs in the historical modeling period, and n represents the number of reference stations participating in the modeling. The latitude of the piercing point for the observation of satellite s at epoch t reference station r; The longitude of the puncture point is the observation value of satellite s from the reference station r at epoch t.

[0089] Based on the center coordinates of the observed values, the puncture point position corresponding to each satellite observation value, and the measured oblique ionospheric delay error value corresponding to each satellite observation value, the measured oblique ionospheric delay model of the satellite is generated by fitting using the least squares method.

[0090] In a preferred embodiment, the measured ionospheric slant delay model of the satellite is generated in the following manner:

[0091] Based on the center coordinates of the observed values, the puncture point position corresponding to each satellite observation, and the measured oblique ionospheric delay error value corresponding to each satellite observation, a polynomial model is used to fit the satellite observations to obtain the measured oblique ionospheric delay model of the satellite:

[0092]

[0093] Where L is the maximum order in the latitude direction; K is the maximum order in the longitude direction. These are the model coefficients. Puncture point Coordinates to the center of observation The difference in latitude; Δλ=λ-λ s Puncture point Coordinates to the center of observation Longitude difference;

[0094] The polynomial fit was calculated using the least squares method:

[0095] X=(H T ·H) -1 ·(H T ·L) (15)

[0096]

[0097] Where X represents the coefficients of the polynomial to be determined, H is the design matrix, each row of which represents the model parameters corresponding to the observations of a certain reference station r on satellite s at epoch t, ​​and L is the measured oblique ionospheric delay error value corresponding to the satellite observations, [·] T This is the transpose of the matrix.

[0098] Based on the measured oblique ionospheric delay error value corresponding to each satellite observation, the residual statistical value of the measured oblique ionospheric delay model is calculated as the model accuracy of the measured oblique ionospheric delay model.

[0099] In actual calculations, the residual statistics of the measured ionospheric oblique delay model are calculated using the following formula:

[0100]

[0101] Among them, Std s As the residual statistics of the measured ionospheric oblique delay model.

[0102] Step S4: Based on the satellite motion trajectory data for the corresponding historical period and the satellite motion trajectory data for the corresponding prediction period, generate the first error model value for the historical period and the second error model value for the prediction period using the IRI model.

[0103] In a preferred embodiment, the output of the IRI model is calculated using the following formula:

[0104]

[0105] in, Let be the error model value corresponding to the satellite's motion trajectory output by the IRI model along the path of the satellite's observation signal from the reference station r at epoch t; MF is the projection function. In latitude Ionospheric delay information calculated by the IRI model at longitude λ and altitude h, where latitude... Longitude λ and altitude h are points on the path of the observation signal from the reference station r to the satellite s. The projection function MF is calculated as follows:

[0106]

[0107] Where z is the zenith distance of the satellite at the reference station, which can be calculated using equation (8); El is the satellite elevation angle, R E Where H is the Earth's radius, and H is the height of the puncture point, which is set to 506.7 km here.

[0108] It should be noted that the IRI model mentioned here can be the IRI-2020 model.

[0109] Step S5: Match and group the measured oblique ionospheric delay error values ​​with the first error model values. For each group, calculate and generate the corresponding systematic deviation and standard deviation based on the corresponding measured oblique ionospheric delay error value and the first error model value; for each second error model value, correct it according to the corresponding systematic deviation to obtain the corresponding oblique ionospheric delay error model value, which is used as the corresponding third error model value.

[0110] In a preferred embodiment, matching and grouping the measured oblique ionospheric delay error value with the first error model value includes:

[0111] By combining the measured oblique ionospheric delay error values ​​consistent with the reference station, satellite, and epoch with the values ​​of the first error model, several matching sets are obtained;

[0112] The matching sets of the reference station and the satellite are grouped to obtain several matching groups. Each matching group contains all measured oblique ionospheric delay error values ​​of one reference station for one satellite and their corresponding first error model values. The result is shown in the following equation:

[0113]

[0114] in, This represents all observation sets from reference station r to satellite s, consisting of the measured oblique ionospheric delay error values ​​from reference station r to satellite s. The first error model value output by the IRI model of the reference station r for satellite s Composition. T0 is the time when modeling is performed, t predictTo predict the coverage time of the model.

[0115] In practice, the systematic deviation and standard deviation are calculated using the following formula:

[0116]

[0117] in, The difference between the measured oblique ionospheric delay error value of the reference station r at epoch t for satellite s and the corresponding first error model value; The systematic deviation value of the reference station r from the satellite s; Let be the standard deviation of the base station r relative to the satellite s.

[0118] In an optional embodiment, the corresponding oblique ionospheric delay error model value is obtained by correcting each second error model value according to the corresponding systematic deviation using the following formula:

[0119]

[0120] in, The value of the oblique ionospheric delay error model for the reference station r relative to the satellite s; This represents the second error model value of the reference station r for satellite s at epoch T0; The systematic deviation of the reference station r from the satellite s; t predict To predict the coverage time of the model.

[0121] Step S6: For each satellite, based on the satellite's motion trajectory data for the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation, and the corresponding third error model value, generate the corresponding prediction oblique ionospheric delay model and the second model accuracy of the prediction oblique ionospheric delay model.

[0122] Specifically, the step of generating a corresponding oblique ionospheric delay model and a second model accuracy of the oblique ionospheric delay model based on the satellite's motion trajectory data for the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation, and the corresponding third error model value includes:

[0123] For each corresponding prediction period, the location of the puncture point is calculated using equations (8), (9), (10), and (11) based on the geographical location information of the corresponding reference station.

[0124] Based on the location of the puncture point, the center coordinates of the satellite observation value are generated using equations (12) and (13);

[0125] Based on the center coordinates of the observed values, the puncture point position corresponding to the satellite's motion trajectory data for each prediction period, and the corresponding third error model value, the satellite's predicted oblique ionospheric delay model is generated using equations (14), (15), (16), and (17).

[0126] The mean of the corresponding standard deviations is used as the second model accuracy. The calculation method is as follows:

[0127]

[0128] in, This represents the mean of the standard deviation.

[0129] Step S7: Send each measured ionospheric oblique delay model, each first model accuracy, each predicted oblique ionospheric delay model, and each second model accuracy to the user terminal for positioning purposes.

[0130] It should be noted that each measured ionospheric oblique delay model, each first model accuracy, each predicted oblique ionospheric delay model, and each second model accuracy will be encoded using the RTCM standard and broadcast to the user terminal for positioning.

[0131] In a preferred embodiment, a flowchart illustrating the localization method for an ionospheric model based on IRI model correction provided by the present invention is shown below. Figure 2 As shown.

[0132] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0133] like Figure 3 As shown, an embodiment of the present invention provides an apparatus for an ionospheric model based on IRI model correction, comprising: a data acquisition module 101, a measured oblique ionospheric delay error value generation module 102, a measured model generation module 103, a model value correction module 104, a prediction model generation module 105, and a model transmission module 106.

[0134] The data acquisition module 101 is used to acquire several satellite observation values ​​of each satellite in the historical period, motion trajectory data of each satellite in the historical period, motion trajectory data of each satellite in the prediction period, pseudorange hardware delay of each reference station, pseudorange hardware delay of each satellite, geographical location information of each reference station, and IRI model.

[0135] The measured oblique ionospheric delay error value generation module 102 is used to calculate and generate the corresponding measured oblique ionospheric delay error value for each satellite observation value based on the pseudorange hardware delay of the corresponding reference station and the pseudorange hardware delay of the corresponding satellite.

[0136] The measured model generation module 103 is used to generate a corresponding measured ionospheric oblique delay model and a first model accuracy of the measured ionospheric oblique delay model for each satellite, based on the corresponding satellite observation value, the corresponding measured oblique ionospheric delay error value and the geographical location information of the corresponding reference station.

[0137] The model value correction module 104 is used to generate a first error model value corresponding to the historical period and a second error model value corresponding to the predicted period based on the satellite motion trajectory data of the corresponding historical period and the satellite motion trajectory data of the corresponding predicted period using an IRI model; match and group the measured oblique ionospheric delay error value with the first error model value; for each group, calculate and generate the corresponding systematic deviation and standard deviation based on the corresponding measured oblique ionospheric delay error value and the first error model value; and correct each second error model value based on the corresponding systematic deviation to obtain the corresponding oblique ionospheric delay error model value, which is used as the corresponding third error model value.

[0138] The prediction model generation module 105 is used to generate a corresponding prediction oblique ionospheric delay model and a second model accuracy of the prediction oblique ionospheric delay model for each satellite, based on the satellite's motion trajectory data for the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation, and the corresponding third error model value.

[0139] The model sending module 106 is used to send each measured ionospheric oblique delay model, each first model accuracy, each predicted oblique ionospheric delay model, and each second model accuracy to the user terminal for positioning.

[0140] It should be noted that the embodiments of the apparatus described above correspond to the embodiments of the present invention described above, and can implement any of the methods described above in the present invention. Furthermore, the embodiments of the apparatus described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Additionally, in the accompanying drawings of the apparatus embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without creative effort.

[0141] Based on the above-described method embodiments of the present invention, a corresponding embodiment of an electronic device is provided.

[0142] An embodiment of the present invention provides an electronic device, including 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, it implements the ionospheric model localization method based on IRI model correction as described in any one of the present invention, or the processor implements the functions of each module in the above-described device embodiments.

[0143] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device.

[0144] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0145] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0146] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0147] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments;

[0148] Another embodiment of the present invention provides a storage medium including a stored computer program, wherein, when the computer program is executed, the device where the storage medium is located executes any of the above-described ionospheric model localization methods based on IRI model correction.

[0149] The aforementioned storage medium is a computer-readable storage medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0150] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0151] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for locating an ionospheric model based on an IRI model correction, characterized in that, include: Acquire several satellite observations for each satellite during historical periods, motion trajectory data for each satellite during historical periods, motion trajectory data for each satellite during predicted periods, pseudorange hardware delay for each reference station, pseudorange hardware delay for each satellite, geographical location information for each reference station, and IRI model. For each satellite observation, the corresponding measured oblique ionospheric delay error value is calculated and generated based on the pseudorange hardware delay of the corresponding reference station and the pseudorange hardware delay of the corresponding satellite. For each satellite, based on the corresponding satellite observations, the corresponding measured oblique ionospheric delay error value, and the geographical location information of the corresponding reference station, a measured ionospheric oblique delay model and the first model accuracy of the measured ionospheric oblique delay model are generated. Based on the satellite motion trajectory data for the corresponding historical period and the satellite motion trajectory data for the corresponding predicted period, a first error model value for the historical period and a second error model value for the predicted period are generated using the IRI model; the measured oblique ionospheric delay error value is matched and grouped with the first error model value. For each group, the corresponding systematic deviation and standard deviation are calculated based on the corresponding measured oblique ionospheric delay error value and the first error model value; for each second error model value, it is corrected according to the corresponding systematic deviation to obtain the corresponding oblique ionospheric delay error model value, which is used as the corresponding third error model value. For each satellite, based on the satellite's motion trajectory data for the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation, and the corresponding third error model value, a corresponding oblique ionospheric delay model and a second model accuracy of the oblique ionospheric delay model are generated. Each measured ionospheric oblique delay model, each first model accuracy, each predicted ionospheric oblique delay model, and each second model accuracy are sent to the user terminal for positioning purposes.

2. The method for locating the ionosphere model based on IRI model correction as described in claim 1, characterized in that, The process of generating a corresponding measured ionospheric oblique delay model and a first model accuracy of the measured ionospheric oblique delay model based on the corresponding satellite observations, the corresponding measured oblique ionospheric delay error value, and the geographical location information of the corresponding reference station includes: For each satellite observation, the location of the corresponding puncture point is calculated using the ionospheric single-layer thin shell assumption, based on the geographical location information of the corresponding reference station. The average value of the puncture point locations is used to generate the center coordinates of the satellite observation values; Based on the center coordinates of the observed values, the puncture point position corresponding to each satellite observation value, and the measured oblique ionospheric delay error value corresponding to each satellite observation value, the measured oblique ionospheric delay model of the satellite is generated by fitting using the least squares method. Based on the measured oblique ionospheric delay error value corresponding to each satellite observation, the residual statistical value of the measured oblique ionospheric delay model is calculated as the first model accuracy of the measured oblique ionospheric delay model.

3. The method for locating the ionosphere model based on IRI model correction as described in claim 1, characterized in that, The step of generating a first error model value for the historical period and a second error model value for the predicted period based on the satellite's motion trajectory data for the corresponding historical period and the satellite's motion trajectory data for the corresponding predicted period using an IRI model includes: Based on the satellite's motion trajectory data for the historical period, a first projection function is calculated and generated, and the first error model value corresponding to the historical period is calculated and generated using the IRI model. Based on the satellite's motion trajectory data for the predicted time period, a second projection function is calculated and generated. Using the IRI model, a second error model value corresponding to the predicted time period is calculated and generated.

4. The method for locating the ionosphere model based on IRI model correction as described in claim 1, characterized in that, The step of matching and grouping the measured oblique ionospheric delay error value with the first error model value includes: By combining the measured oblique ionospheric delay error values ​​consistent with the reference station, satellite, and epoch with the values ​​of the first error model, several matching sets are obtained; The matching sets that are the same for the base station and the satellite are grouped to obtain several matching groups.

5. The method for locating the ionosphere model based on IRI model correction as described in claim 1, characterized in that, The systematic deviation and standard deviation are calculated using the following formula: in, The difference between the measured oblique ionospheric delay error value of the reference station r at epoch t for satellite s and the corresponding first error model value; This represents the measured oblique ionospheric delay error value; This is the value of the first error model; The systematic deviation of the base station r from the satellite s; T is the total number of epochs in the historical modeling period; Let be the standard deviation of the base station r relative to the satellite s.

6. The method for locating the ionosphere model based on IRI model correction as described in claim 5, characterized in that, The following formula is used to correct each second error model value based on the corresponding systematic deviation, thus obtaining the corresponding oblique ionospheric delay error model value: in, The value of the oblique ionospheric delay error model for the reference station r relative to the satellite s; The second error model value for satellite s is given by reference station r at epoch T0; T0 is the time when modeling was performed. The systematic deviation of the reference station r from the satellite s; t predict To predict the coverage time of the model.

7. The method for locating the ionosphere model based on IRI model correction as described in claim 1, characterized in that, The process of generating a corresponding oblique ionospheric delay model and a second model accuracy based on the satellite's motion trajectory data for the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation, and the corresponding third error model value includes: For each predicted period, the location of the puncture point is calculated using the ionospheric single-layer thin shell assumption based on the geographical location information of the corresponding reference station and the satellite's motion trajectory data for each period. The average value of the corresponding puncture point locations is taken to generate the center coordinates of the satellite observation values. Based on the center coordinates of the observed values, the puncture point position corresponding to the satellite's motion trajectory data for each prediction period, and the third error model value corresponding to the satellite's motion trajectory data for each prediction period, a satellite prediction oblique ionospheric delay model is generated by fitting using the least squares method. The average of the standard deviations corresponding to the satellites is used as the second model accuracy of the corresponding predictive oblique ionospheric delay model.

8. A positioning device based on an ionospheric model modified from an IRI model, characterized in that, include: The system includes a data acquisition module, a measured oblique ionospheric delay error value generation module, a measured model generation module, a model value correction module, a prediction model generation module, and a model transmission module. The data acquisition module is used to acquire several satellite observations of each satellite during a historical period, motion trajectory data of each satellite during a historical period, motion trajectory data of each satellite during a predicted period, pseudorange hardware delay of each reference station, pseudorange hardware delay of each satellite, geographical location information of each reference station, and IRI model. The measured oblique ionospheric delay error value generation module is used to calculate and generate the corresponding measured oblique ionospheric delay error value for each satellite observation value based on the pseudorange hardware delay of the corresponding reference station and the pseudorange hardware delay of the corresponding satellite. The measured model generation module is used to generate a corresponding measured ionospheric oblique delay model and a first model accuracy of the measured ionospheric oblique delay model for each satellite, based on the corresponding satellite observation value, the corresponding measured oblique ionospheric delay error value and the geographical location information of the corresponding reference station. The model value correction module is used to generate a first error model value corresponding to the historical period and a second error model value corresponding to the predicted period based on the satellite motion trajectory data of the corresponding historical period and the satellite motion trajectory data of the corresponding predicted period through the IRI model; and to match and group the measured oblique ionospheric delay error value with the first error model value. For each group, the corresponding systematic deviation and standard deviation are calculated based on the corresponding measured oblique ionospheric delay error value and the first error model value; for each second error model value, it is corrected according to the corresponding systematic deviation to obtain the corresponding oblique ionospheric delay error model value, which is used as the corresponding third error model value. The prediction model generation module is used to generate a corresponding prediction oblique ionospheric delay model and a second model accuracy of the prediction oblique ionospheric delay model for each satellite, based on the satellite's motion trajectory data for the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation, and the corresponding third error model value. The model transmission module is used to send each measured ionospheric oblique delay model, each first model accuracy, each predicted ionospheric oblique delay model, and each second model accuracy to the user terminal for positioning purposes.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it can implement the localization method of the ionospheric model based on IRI model correction as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program can implement the ionospheric model localization method based on IRI model correction as described in any one of claims 1 to 7.

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