Ionized layer model positioning method and device based on IRI model correction, electronic equipment and storage medium

Abstract

The invention discloses an ionosphere model positioning method and device based on IRI model correction, electronic equipment and a storage medium, and the method comprises the steps: obtaining satellite observation values of satellites, and carrying out the calculation of each satellite observation value, and generating an actual measurement inclined ionosphere delay error value; according to the actually measured oblique ionosphere delay error value and the satellite observation value, generating an actually measured ionosphere oblique delay model and a first model precision of the actually measured ionosphere oblique delay model; generating an error model value by using an IRI model in combination with the historical trajectory and the predicted trajectory; correcting the error model value of the prediction time period according to the actually measured oblique ionosphere delay error value and the error model value of the historical time period, and generating a prediction oblique ionosphere delay model and second model precision according to the corrected model value; and sending the actually measured ionosphere oblique delay model, the first model precision, the predicted oblique ionosphere delay model and the second model precision to a user side for positioning, thereby improving the positioning precision.

Description

Technical Field

[0001] The present invention relates to the technical field of ionospheric delay modeling, and in particular to a positioning method, device, electronic equipment and storage medium based on an ionospheric model corrected by an IRI model. Background Art

[0002] In the global navigation satellite system (GNSS) positioning process, ionospheric delay error is a key factor affecting the accuracy and convergence time of single point positioning (SPP), precise point positioning (PPP) and PPP-RTK positioning technology widely used in recent years. Therefore, in order to improve positioning accuracy and accelerate positioning convergence speed, providing an accurate and reliable ionospheric delay error estimate is the key to achieving high-precision positioning.

[0003] However, we are currently in the active period of the 25th solar activity cycle. The charged particles in the ionosphere are more active due to the high-energy solar radiation, and the ionosphere shows complex changes. Therefore, the traditional regional ionosphere modeling method using a simple polynomial model has insufficient model accuracy and cannot meet the needs of high-precision positioning. The ionosphere mathematical model method represented by the ionosphere oblique delay model in the prior art collects the observation data of the reference station for modeling. Although it can avoid the projection function error and provide a high-precision delay correction within a small range, the reference point is far away from the actual observation value after a long time due to the high-speed motion of the satellite, resulting in a short validity period and the need for frequent updates. It is also affected by network transmission delays and data loss, which limits its application effect. Summary of the invention

[0004] The embodiments of the present invention provide a positioning method, device, electronic device and storage medium based on an ionospheric model corrected by an IRI model. By implementing the present invention, it is possible to increase the validity period of the ionospheric slant delay model, provide users with a more stable and durable ionospheric slant delay model for global navigation satellite system positioning, thereby improving positioning accuracy.

[0005] An embodiment of the present invention provides a positioning method based on an ionospheric model corrected by an IRI model, comprising:

[0006] Obtain several satellite observation values ​​of each satellite in the historical period, the motion trajectory data of each satellite in the historical period, the motion trajectory data of each satellite in the prediction period, the pseudorange hardware delay of each reference station, the pseudorange hardware delay of each satellite, the geographic location information of each reference station and the IRI model;

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

[0008] For each satellite, a measured ionospheric slant delay model and a first model accuracy of the measured ionospheric slant delay model are generated according to the corresponding satellite observation value, the corresponding measured slant ionospheric delay error value and the geographical location information of the corresponding reference station;

[0009] According to the motion trajectory data of the satellite in the corresponding historical period and the motion trajectory data of the satellite in the corresponding prediction period, a first error model value corresponding to the historical period and a second error model value corresponding to the prediction period are respectively generated through the IRI model; the measured oblique ionospheric delay error value and the first error model value are matched and grouped; for each group, according to the corresponding measured oblique ionospheric delay error value and the first error model value, a corresponding systematic deviation and a standard deviation are calculated and generated; for each second error model value, a correction is made according to the corresponding systematic deviation to obtain a corresponding oblique ionospheric delay error model value as the corresponding third error model value;

[0010] For each satellite, a corresponding predicted oblique ionospheric delay model and a second model accuracy of the predicted oblique ionospheric delay model are generated according to the motion trajectory data of the satellite in the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation and the corresponding third error model value;

[0011] Each measured ionospheric slant delay model, each first model accuracy, each predicted slant ionospheric delay model and each second model accuracy are sent to the user end for use by the user end for positioning.

[0012] Further, generating a corresponding measured ionospheric slant delay model and a first model accuracy of the measured ionospheric slant delay model according to the corresponding satellite observation value, the corresponding measured slant ionospheric delay error value and the corresponding geographical location information of the reference station includes:

[0013] For each satellite observation value, according to the geographical location information of the corresponding reference station, the corresponding puncture point position is calculated using the ionospheric single-layer thin shell hypothesis;

[0014] Taking an average value of the puncture point positions to generate the satellite's observation value center coordinates;

[0015] Generate a satellite's measured ionospheric slant delay model by fitting through the least squares method according to the observation value center coordinates, the puncture point position corresponding to each satellite observation value, and the measured slant ionospheric delay error value corresponding to each satellite observation value;

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

[0017] Further, the method of generating a first error model value corresponding to the historical period and a second error model value corresponding to the prediction period respectively according to the motion trajectory data of the satellite in the corresponding historical period and the motion trajectory data of the satellite in the corresponding prediction period by the IRI model includes:

[0018] According to the motion trajectory data of the satellite in the historical period, a first projection function is calculated and generated, and a first error model value corresponding to the historical period is calculated and generated using the IRI model;

[0019] A second projection function is calculated and generated based on the motion trajectory data of the satellite during the prediction period, and a second error model value corresponding to the prediction period is calculated and generated using the IRI model.

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

[0021] Combining the measured slant ionospheric delay error values ​​consistent with the reference station, satellite and epoch with the first error model value to obtain a plurality of matching sets;

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

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

[0024]

[0025]

[0026]

[0027] in, is the difference between the measured slant ionospheric delay error value of the reference station r for satellite s at epoch t and the corresponding first error model value; is the systematic deviation of the reference station r to the satellite s; is the standard deviation of the reference station r with respect to the satellite s.

[0028] Furthermore, the following formula is used to correct each second error model value according to the corresponding systematic deviation to obtain the corresponding oblique ionospheric delay error model value:

[0029]

[0030] in, is the model value of the slant ionospheric delay error of the reference station r to the satellite s; is the second error model value of the reference station r to the satellite s at epoch T0; is the systematic deviation of the reference station r to the satellite s; t predict Covering time for prediction models.

[0031] Further, the generating of the corresponding predicted oblique ionospheric delay model and the second model accuracy of the predicted oblique ionospheric delay model according to the motion trajectory data of the satellite in 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:

[0032] For the satellite's motion trajectory data in each prediction period, according to the geographical location information of the corresponding reference station, the corresponding puncture point position is calculated using the ionospheric single-layer thin shell hypothesis;

[0033] The corresponding puncture point positions are averaged to generate the center coordinates of the observation values ​​corresponding to the satellite;

[0034] Generate a predicted oblique ionospheric delay model of the satellite by fitting through the least squares method according to the central coordinates of the observation value, the puncture point position corresponding to the motion trajectory data of the satellite in each prediction period, and the third error model value corresponding to the motion trajectory data of the satellite in each prediction period;

[0035] The standard deviations corresponding to the satellites are averaged, and the obtained average value is used as the second model accuracy of the corresponding predicted oblique ionospheric delay model.

[0036] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0037] An embodiment of the present invention provides a positioning device based on an ionospheric model corrected by an IRI model, 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 sending module.

[0038] The data acquisition module is used to acquire a number of satellite observation values ​​of each satellite in the historical period, the motion trajectory data of each satellite in the historical period, the motion trajectory data of each satellite in the prediction period, the pseudorange hardware delay of each reference station, the pseudorange hardware delay of each satellite, the geographical location information of each reference station and the IRI model;

[0039] The measured oblique ionospheric delay error value generating module is used to calculate and generate the corresponding measured oblique ionospheric delay error value for each satellite observation value according to the pseudorange hardware delay of the corresponding reference station and the pseudorange hardware delay of the corresponding satellite;

[0040] The measured model generation module is used to generate, for each satellite, a corresponding measured ionospheric slant delay model and a first model accuracy of the measured ionospheric slant delay model according to corresponding satellite observation values, corresponding measured slant ionospheric delay error values ​​and corresponding geographic location information of a reference station;

[0041] 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 prediction period respectively through the IRI model according to the motion trajectory data of the satellite in the corresponding historical period and the motion trajectory data of the satellite in the corresponding prediction period; match and group the measured oblique ionospheric delay error value with the first error model value; for each group, calculate and generate a corresponding systematic deviation and a standard deviation according to 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 a corresponding oblique ionospheric delay error model value as the corresponding third error model value;

[0042] The prediction model generation module is used to generate, for each satellite, a corresponding predicted oblique ionospheric delay model and a second model accuracy of the predicted oblique ionospheric delay model according to the motion trajectory data of the satellite in the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation and the corresponding third error model value;

[0043] The model sending module is used to send each measured ionospheric slant delay model, each first model accuracy, each predicted slant ionospheric delay model and each second model accuracy to the user terminal for use in positioning.

[0044] Based on the above method embodiment, the present invention provides a corresponding electronic device embodiment.

[0045] 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, the positioning method based on the ionospheric model corrected by the IRI model as described in any one of the above method embodiments can be implemented.

[0046] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0047] An embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the positioning method based on the ionospheric model corrected by the IRI model as described in any one of the above method embodiments can be implemented.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The embodiment of the present invention provides a positioning method, device, electronic device and storage medium based on an ionospheric model corrected by an IRI model. The method calculates the measured oblique ionospheric delay error value of each satellite observation value based on the pseudorange hardware delay of the reference station and the pseudorange hardware delay of the satellite; then generates a measured ionospheric oblique delay model and a corresponding first model accuracy according to the measured oblique ionospheric delay error value and the geographical location of the reference station; combines historical trajectories and predicted trajectories, and uses the IRI model to generate a first error model value corresponding to a historical period and a second error model value corresponding to a predicted period; corrects the second error model value according to the measured oblique ionospheric delay error value and the first error model value, and generates a predicted oblique ionospheric delay model and a corresponding second model accuracy according to the corrected model value; sends 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 by the user terminal.

[0050] The present invention calculates the measured oblique ionospheric delay error value by using the measured satellite observation value of the reference station and constructs the measured ionospheric oblique delay model; on the basis of the measured oblique ionospheric delay error value supplemented with correction, the ionospheric delay error on the subsequent trajectory of the satellite is predicted in the short term by using the IRI model belonging to the physical model to establish a prediction model. The above two models are broadcasted to the user end at the same time. When the user cannot update the ionospheric oblique delay model in time, the prediction model can be used to correct the ionospheric delay error in the positioning process, ensuring that the positioning result is in a high-precision usable state most of the time. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of a positioning method based on an ionospheric model corrected by an IRI model provided in one embodiment of the present invention.

[0052] Figure 2 Another embodiment of the present invention provides a flow chart of a positioning method based on an ionospheric model corrected by an IRI model.

[0053] Figure 3 It is a structural schematic diagram of a positioning device based on an ionospheric model corrected by an IRI model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] like Figure 1 As shown, an embodiment of the present invention provides a positioning method based on an ionospheric model corrected by an IRI model, comprising at least the following steps:

[0056] Step S1, obtaining a number of satellite observation values ​​of each satellite in a historical period, motion trajectory data of each satellite in a historical period, motion trajectory data of each satellite in a prediction period, pseudorange hardware delay of each reference station, pseudorange hardware delay of each satellite, geographic location information of each reference station and an IRI model;

[0057] Preferably, the pseudorange hardware delay of the reference station and the pseudorange hardware delay of the satellite are defined as follows:

[0058] DCB r，1|2 =B r，1 -B r，2 (1)

[0059]

[0060] Among them, DCB r，1|2 is the pseudorange hardware delay of the reference station on frequencies f1 and f2; B is the pseudorange hardware delay of the satellite on frequencies f1 and f2; r，1 is the code delay of the reference station at frequency f1; B r，2 is the code delay of the reference station at frequency f2; is the code delay of the satellite at frequency f1; is the code delay of the satellite at frequency f2;

[0061] The IRI model obtained already includes the necessary input data of the model, including CCIR data set, URSI data set, DGRF data set, IGRF data, MCSAT data, IG index, AP index, and F107 index. Among them, the necessary input data included above can be downloaded from the official data server of International Reference Ionosphere. CCIR data, URSI data and MCSAT data need to cover 2011 to 2022, DGRF data needs to cover 5-year intervals from 1945 to 2015, and IGRF data needs to be updated in 2020. IG index, AP index, and F107 index are updated monthly and need to be updated in time.

[0062] Specifically, the base station pseudorange hardware delay and the satellite pseudorange hardware delay are downloaded from a GNSS service agency or estimated by the user.

[0063] Step S2: for each satellite observation value, the corresponding measured oblique ionospheric delay error value is calculated and generated according to the pseudorange hardware delay of the corresponding reference station and the pseudorange hardware delay of the corresponding satellite;

[0064] In a preferred embodiment, the measured oblique ionospheric delay error value can be calculated and generated by a phase smoothing pseudorange method, and the specific steps include:

[0065] The geometry-free observation values ​​of pseudorange and carrier are obtained by performing geometry-free combination of GNSS observation equations; the GNSS observation equation is as follows:

[0066]

[0067] Where E[·] is the expectation operator, r is the reference station identifier, s is the satellite identifier, and t is the epoch time. is the pseudo-range observation value from the reference station r to the satellite s at time t, is the carrier observation value from the reference station r to the satellite s at time t, λ s is the carrier wavelength of the observation value of the i-th frequency point of satellite s, is the distance between the base station r and the satellite s at time t, dt r (t) is the receiver clock error at time t, dt s (t) is the satellite clock error at time t, Trop r (t) is the tropospheric delay of base station r at time t, is the ionospheric delay from the reference station r to the satellite s at time t, f0 is the reference frequency (using 1572.42MHz), f i is the frequency of the observed value at the i-th frequency point, b r，i (t) is the carrier hardware delay of the i-th frequency point of the reference station r at time t, is the carrier hardware delay of the i-th frequency point of satellite s at time t, N i is the carrier ambiguity of the observation value at the i-th frequency point.

[0068] The obtained pseudorange and carrier geometry-free observation values ​​are as follows:

[0069]

[0070] in, is the pseudorange observation value without geometry combination; is the geometry-free carrier observation value, r is the reference station identification number, s is the satellite identification number, λ4 represents the geometry-free carrier wavelength, STEC represents the slant path total electron content (Slant TEC) on the GNSS signal path, f1 and f2 represent the corresponding frequencies of the two observations involved in the calculation, c is the speed of light in vacuum, DCB r，|2 is the differential code deviation of the receiver at frequencies f1 and f2, is the satellite differential code deviation at frequencies f1 and f2, b r，f is the carrier hardware delay of the receiver corresponding to the frequency point f; is the carrier hardware delay of the satellite corresponding to the frequency point f, λ f is the carrier wavelength at frequency f, is the ambiguity cycle number of the carrier observation value.

[0071] Number of ambiguity cycles of carrier observations The carrier hardware delay b of the receiver corresponding to the frequency point f r，f The carrier hardware delay of the satellite corresponding to the frequency point f It is stable when there is no cycle slip in a short time. Therefore, after eliminating the cycle slip, the short-term pseudorange and the carrier's geometry-free observation value are averaged to obtain the following relationship:

[0072]

[0073] in, It represents the mean of the sum of pseudorange geometry-free combination observations and carrier geometry-free combination observations in a continuous arc segment.

[0074] Solve equation (5) and equation (4) together to generate the phase smoothed pseudorange observation value corresponding to the satellite observation value:

[0075]

[0076] in, is the phase smoothed pseudorange observation value.

[0077] Formula (6) is solved together with Formula (4), and the measured oblique ionospheric delay error value corresponding to the satellite observation value is generated according to the pseudorange hardware delay of the corresponding reference station and the pseudorange hardware delay of the satellite:

[0078]

[0079] in, is the measured oblique ionospheric delay error value.

[0080] Optionally, the measured oblique ionospheric delay error value can also be calculated and generated by a non-difference non-combined PPP method.

[0081] Step S3: for each satellite, a measured ionospheric slant delay model and a first model accuracy of the measured ionospheric slant delay model are generated according to the corresponding satellite observation value, the corresponding measured ionospheric slant delay error value and the geographical location information of the corresponding reference station;

[0082] In a preferred embodiment, the measured ionospheric slant delay model and the first model accuracy of the measured ionospheric slant delay model generated according to the corresponding satellite observation value, the corresponding measured slant ionospheric delay error value and the corresponding geographical location information of the reference station include:

[0083] For each corresponding satellite observation value, according to the geographical location information of the corresponding reference station, the corresponding puncture point position is calculated using the ionospheric single-layer thin shell hypothesis;

[0084] In actual operation, the corresponding puncture point position is calculated by the following formula:

[0085]

[0086] Among them, is the zenith distance of the satellite at the base station, is the satellite azimuth, is the satellite altitude angle,

[0087] is the radius of the earth, is the height of the puncture point, and are the latitude and longitude of the reference station, and are the latitude and longitude of the puncture point. Among them, the satellite azimuth and satellite altitude angle are calculated through the satellite position and the base station position.

[0088] Taking an average value of the puncture point positions to generate the satellite's observation value center coordinates;

[0089] In the specific implementation, the satellite observation center coordinates are generated by the following formula:

[0090]

[0091]

[0092] Where T is the total number of epochs in the historical modeling period, n is the number of reference stations involved in the modeling, is the latitude of the puncture point of the observation value of satellite s at the reference station r at epoch t; is the longitude of the puncture point of the observation value of satellite s at the reference station r at epoch t.

[0093] Generate a satellite's measured ionospheric slant delay model by fitting through the least squares method according to the observation value center coordinates, the puncture point position corresponding to each satellite observation value, and the measured slant ionospheric delay error value corresponding to each satellite observation value;

[0094] In a preferred embodiment, the measured ionospheric slant delay model of the satellite is generated by:

[0095] According to the central coordinates of the observation value, 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 satellite observation value is fitted using a polynomial model to obtain the measured ionospheric oblique delay model of the satellite:

[0096]

[0097] Among them, L is the maximum order in the latitude difference direction; K is the maximum order in the longitude difference direction, is the model coefficient, For puncture point To the observation center coordinates The latitude difference; Δλ=λ-λ s For puncture point To the observation center coordinates The longitude difference

[0098] The polynomial fit is calculated using the least squares method:

[0099] X=(H T ·H) -1 ·(H T ·L) (15)

[0100]

[0101] Where X is the coefficient of the polynomial to be calculated, H is the design matrix, each row in the matrix represents the model parameters corresponding to the observation value of a certain reference station r on satellite s at epoch t, ​​L is the measured oblique ionospheric delay error value corresponding to the satellite observation value, [·] T is the transpose of the matrix.

[0102] According to the measured oblique ionospheric delay error value corresponding to each satellite observation value, the residual statistical value of the measured ionospheric slant delay model is calculated as the model accuracy of the measured ionospheric slant delay model.

[0103] In actual calculation, the residual statistical value of the measured ionospheric slant delay model is calculated by the following formula:

[0104]

[0105] Among them, Std s As the residual statistic of the measured ionospheric slant delay model.

[0106] Step S4, generating a first error model value corresponding to the historical period and a second error model value corresponding to the prediction period respectively through the IRI model according to the motion trajectory data of the satellite in the corresponding historical period and the motion trajectory data of the satellite in the corresponding prediction period;

[0107] In a preferred embodiment, the output of the IRI model is calculated by the following formula:

[0108]

[0109] in, is the error model value corresponding to the satellite motion trajectory output by the IRI model on the observation signal path of satellite s at the reference station r at epoch t; MF is the projection function, For latitude The ionospheric delay information calculated by the IRI model at longitude λ and altitude h, where latitude The longitude λ and height h are points on the signal path of the base station r observing the satellite s. The calculation method of the projection function MF is:

[0110]

[0111] Where z is the zenith distance of the satellite at the reference station, which can be calculated by formula (8); El is the satellite altitude angle, R E is the radius of the earth, H is the height of the puncture point, and the puncture point height H is set to 506.7km here.

[0112] It should be noted that the IRI model described here may be the IRI-2020 model.

[0113] Step S5, matching and grouping the measured oblique ionospheric delay error value and the first error model value. For each group, according to the corresponding measured oblique ionospheric delay error value and the first error model value, calculate and generate the corresponding systematic deviation and standard deviation; for each second error model value, correct it according to the corresponding systematic deviation to obtain the corresponding oblique ionospheric delay error model value as the corresponding third error model value;

[0114] In a preferred embodiment, matching and grouping the measured oblique ionospheric delay error value with the first error model value includes:

[0115] Combining the measured slant ionospheric delay error values ​​consistent with the reference station, satellite and epoch with the first error model value to obtain a plurality of matching sets;

[0116] Group the matching sets with the same reference station and satellite to obtain several matching groups. Each matching group contains all the measured oblique ionospheric delay error values ​​of a reference station for a satellite and the corresponding first error model value. The result is as follows:

[0117]

[0118] in, Represents all observation value groups of the reference station r for satellite s, which are the measured oblique ionospheric delay error data of the reference station r for satellite s and the first error model value of the IRI model output of the reference station r for satellite s T0 is the time when modeling is performed, t predict Covering time for prediction models.

[0119] In actual operation, the systematic deviation and standard deviation are calculated by the following formula:

[0120]

[0121] in, is the difference between the measured slant ionospheric delay error value of the reference station r for satellite s at epoch t and the corresponding first error model value; is the systematic deviation of the reference station r to the satellite s; is the standard deviation of the reference station r with respect to the satellite s.

[0122] In an optional embodiment, the following formula is used to correct each second error model value according to the corresponding systematic deviation to obtain the corresponding oblique ionospheric delay error model value:

[0123]

[0124] in, is the model value of the oblique ionospheric delay error of the reference station to the satellite; is the second error model value of the epoch reference station to the satellite; is the systematic deviation value of the reference station to the satellite; is the coverage time of the prediction model.

[0125] Step S6: for each satellite, according to the motion trajectory data of the satellite in 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 a corresponding predicted oblique ionospheric delay model and a second model accuracy of the predicted oblique ionospheric delay model;

[0126] Specifically, generating a corresponding predicted oblique ionospheric delay model and a second model accuracy of the predicted oblique ionospheric delay model according to the motion trajectory data of the satellite in 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:

[0127] For each corresponding prediction period, the satellite trajectory data is used to calculate the corresponding puncture point position according to the geographical location information of the corresponding reference station through equations (8), (9), (10), and (11);

[0128] According to the position of the puncture point, the center coordinates of the satellite observation value are generated through equations (12) and (13);

[0129] According to the central coordinates of the observation value, the puncture point position corresponding to the motion trajectory data of the satellite in each prediction period and the corresponding third error model value, the predicted oblique ionospheric delay model of the satellite is generated through equations (14), (15), (16) and (17);

[0130] Calculate the mean of the corresponding standard deviation as the second model accuracy of the model, and the calculation method is:

[0131]

[0132] in, is the mean standard deviation.

[0133] Step S7: Send each measured ionospheric slant delay model, each first model accuracy, each predicted slant ionospheric delay model and each second model accuracy to the user terminal for use in positioning.

[0134] It should be noted that each measured ionospheric slant delay model, each first model accuracy, each predicted slant ionospheric delay model and each second model accuracy generated are encoded using the RTCM standard and broadcast to the user end for positioning by the user end.

[0135] In a preferred embodiment, the flow chart of a positioning method based on an ionospheric model corrected by an IRI model provided by the present invention is as follows: Figure 2 shown.

[0136] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0137] like Figure 3 As shown, an embodiment of the present invention provides an ionospheric model device based on IRI model correction, including: 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 sending module 106.

[0138] The data acquisition module 101 is used to acquire a number of satellite observation values ​​of each satellite in the historical period, the motion trajectory data of each satellite in the historical period, the motion trajectory data of each satellite in the prediction period, the pseudorange hardware delay of each reference station, the pseudorange hardware delay of each satellite, the geographical location information of each reference station and the IRI model;

[0139] The measured oblique ionospheric delay error value generating module 102 is used to calculate and generate the corresponding measured oblique ionospheric delay error value for each satellite observation value according to the pseudorange hardware delay of the corresponding reference station and the pseudorange hardware delay of the corresponding satellite;

[0140] The measured model generation module 103 is used to generate, for each satellite, a corresponding measured ionospheric slant delay model and a first model accuracy of the measured ionospheric slant delay model according to corresponding satellite observation values, corresponding measured slant ionospheric delay error values ​​and corresponding geographic location information of a reference station;

[0141] 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 prediction period respectively through the IRI model according to the motion trajectory data of the satellite in the corresponding historical period and the motion trajectory data of the satellite in the corresponding prediction period; match and group the measured oblique ionospheric delay error value and the first error model value; for each group, calculate and generate a corresponding systematic deviation and a standard deviation according to 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 a corresponding oblique ionospheric delay error model value as the corresponding third error model value;

[0142] The prediction model generation module 105 is used to generate, for each satellite, a corresponding predicted oblique ionospheric delay model and a second model accuracy of the predicted oblique ionospheric delay model according to the motion trajectory data of the satellite in the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation and the corresponding third error model value;

[0143] The model sending module 106 is used to send each measured ionospheric slant delay model, each first model accuracy, each predicted slant ionospheric delay model and each second model accuracy to the user terminal for use in positioning.

[0144] It should be noted that the embodiments of the device described above correspond to the above embodiments of the present invention, and can implement any of the methods described above in the present invention. In addition, the embodiments of the above-mentioned device are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0145] Based on the above method embodiment of the present invention, a corresponding electronic device embodiment is provided.

[0146] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the positioning method based on the ionospheric model corrected by the IRI model as described in any one of the present invention is implemented, or, when the processor executes the computer program, the functions of the modules in the above-mentioned device embodiments are implemented.

[0147] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0148] The terminal device may be a computing device such as a desktop computer, a notebook, a palmtop computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0149] The processor may be a central processing unit (CPU), 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 may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0150] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the 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 for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0151] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0152] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute any one of the positioning methods of the present invention based on the ionospheric model corrected by the IRI model.

[0153] The storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased 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 electric carrier signals and telecommunication signals.

[0154] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0155] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A positioning method based on an ionospheric model corrected by an IRI model, characterized in that: include: Obtain several satellite observation values ​​of each satellite in the historical period, the motion trajectory data of each satellite in the historical period, the motion trajectory data of each satellite in the prediction period, the pseudorange hardware delay of each reference station, the pseudorange hardware delay of each satellite, the geographic location information of each reference station and the IRI model; For each satellite observation value, the corresponding measured oblique ionospheric delay error value is calculated based on the pseudorange hardware delay of the corresponding reference station and the pseudorange hardware delay of the corresponding satellite; For each satellite, a measured ionospheric slant delay model and a first model accuracy of the measured ionospheric slant delay model are generated according to the corresponding satellite observation value, the corresponding measured slant ionospheric delay error value and the geographical location information of the corresponding reference station; According to the motion trajectory data of the satellite in the corresponding historical period and the motion trajectory data of the satellite in the corresponding predicted period, a first error model value corresponding to the historical period and a second error model value corresponding to the predicted period are respectively generated through the IRI model; the measured oblique ionospheric delay error value is matched and grouped with the first error model value; For each group, a corresponding systematic deviation and a 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, a correction is performed based on the corresponding systematic deviation to obtain a corresponding oblique ionospheric delay error model value as the corresponding third error model value; For each satellite, a corresponding predicted oblique ionospheric delay model and a second model accuracy of the predicted oblique ionospheric delay model are generated according to the motion trajectory data of the satellite in the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation and the corresponding third error model value; Each measured ionospheric slant delay model, each first model accuracy, each predicted slant ionospheric delay model and each second model accuracy are sent to the user end for use by the user end for positioning.

2. The positioning method based on the ionospheric model corrected by the IRI model as claimed in claim 1, characterized in that: The method of generating a corresponding measured ionospheric slant delay model and a first model accuracy of the measured ionospheric slant delay model according to the corresponding satellite observation value, the corresponding measured slant ionospheric delay error value and the geographical location information of the corresponding reference station comprises: For each satellite observation value, according to the geographical location information of the corresponding reference station, the corresponding puncture point position is calculated using the ionospheric single-layer thin shell hypothesis; Taking an average value of the puncture point positions to generate the satellite's observation value center coordinates; Generate a satellite's measured ionospheric slant delay model by fitting through the least squares method according to the observation value center coordinates, the puncture point position corresponding to each satellite observation value, and the measured slant ionospheric delay error value corresponding to each satellite observation value; According to the measured slant ionospheric delay error value corresponding to each satellite observation value, the residual statistical value of the measured ionospheric slant delay model is calculated as the first model accuracy of the measured ionospheric slant delay model.

3. The positioning method based on the ionospheric model corrected by the IRI model as claimed in claim 1, characterized in that: The method of generating a first error model value corresponding to the historical period and a second error model value corresponding to the prediction period respectively according to the motion trajectory data of the satellite in the corresponding historical period and the motion trajectory data of the satellite in the corresponding prediction period by using the IRI model includes: According to the motion trajectory data of the satellite in the historical period, a first projection function is calculated and generated, and a first error model value corresponding to the historical period is calculated and generated using the IRI model; A second projection function is calculated and generated based on the motion trajectory data of the satellite during the prediction period, and a second error model value corresponding to the prediction period is calculated and generated using the IRI model.

4. The positioning method based on the ionospheric model corrected by the IRI model as claimed in claim 1, characterized in that: The matching and grouping of the measured oblique ionospheric delay error value and the first error model value comprises: Combining the measured slant ionospheric delay error values ​​consistent with the reference station, satellite and epoch with the first error model value to obtain a plurality of matching sets; The matching sets with the same base station and satellite are grouped to obtain several matching groups.

5. The positioning method based on the ionospheric model corrected by the IRI model as claimed in claim 1, characterized in that: The systematic deviation and standard deviation are calculated using the following formula: in, is the difference between the measured slant ionospheric delay error value of the reference station r for satellite s at epoch t and the corresponding first error model value; is the systematic deviation of the reference station r to the satellite s; is the standard deviation of the reference station r with respect to the satellite s.

6. The positioning method based on the ionospheric model corrected by the IRI model as claimed in claim 5, characterized in that: The following formula is used to correct each second error model value according to the corresponding systematic deviation to obtain the corresponding oblique ionospheric delay error model value: in, is the model value of the slant ionospheric delay error of the reference station r to the satellite s; is the second error model value of the reference station r to the satellite s at epoch T0; is the systematic deviation of the reference station r to the satellite s; t predict Covering time for prediction models.

7. The positioning method based on the ionospheric model corrected by the IRI model as claimed in claim 1, characterized in that: The method of generating a corresponding predicted oblique ionospheric delay model and a second model accuracy of the predicted oblique ionospheric delay model according to the motion trajectory data of the satellite in the corresponding prediction period, the geographical location information of the corresponding reference station, the corresponding standard deviation and the corresponding third error model value comprises: For the satellite's motion trajectory data in each prediction period, according to the geographical location information of the corresponding reference station, the corresponding puncture point position is calculated using the ionospheric single-layer thin shell hypothesis; The corresponding puncture point positions are averaged to generate the center coordinates of the observation values ​​corresponding to the satellite; Generate a predicted oblique ionospheric delay model of the satellite by fitting through the least squares method according to the central coordinates of the observation value, the puncture point position corresponding to the motion trajectory data of the satellite in each prediction period, and the third error model value corresponding to the motion trajectory data of the satellite in each prediction period; The standard deviations corresponding to the satellites are averaged, and the obtained average value is used as the second model accuracy of the corresponding predicted oblique ionospheric delay model.

8. A positioning device based on an ionospheric model corrected by an IRI model, characterized in that: include: Data acquisition module, measured oblique ionospheric delay error value generation module, measured model generation module, model value correction module, prediction model generation module and model sending module; The data acquisition module is used to acquire a number of satellite observation values ​​of each satellite in the historical period, the motion trajectory data of each satellite in the historical period, the motion trajectory data of each satellite in the prediction period, the pseudorange hardware delay of each reference station, the pseudorange hardware delay of each satellite, the geographical location information of each reference station and the IRI model; The measured oblique ionospheric delay error value generating module is used to calculate and generate the corresponding measured oblique ionospheric delay error value for each satellite observation value according to 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, for each satellite, a corresponding measured ionospheric slant delay model and a first model accuracy of the measured ionospheric slant delay model according to corresponding satellite observation values, corresponding measured slant ionospheric delay error values ​​and corresponding geographic location information of a 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 through the IRI model according to the motion trajectory data of the satellite in the corresponding historical period and the motion trajectory data of the satellite in the corresponding predicted period; and match and group the measured oblique ionospheric delay error value with the first error model value; For each group, a corresponding systematic deviation and a 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, a correction is performed based on the corresponding systematic deviation to obtain a corresponding oblique ionospheric delay error model value as the corresponding third error model value; The prediction model generation module is used to generate, for each satellite, a corresponding predicted oblique ionospheric delay model and a second model accuracy of the predicted oblique ionospheric delay model according to the motion trajectory data of the satellite in 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 sending module is used to send each measured ionospheric slant delay model, each first model accuracy, each predicted slant ionospheric delay model and each second model accuracy to the user terminal for use in positioning.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the positioning method based on the ionospheric model corrected by the IRI model as described in any one of claims 1 to 7 can be implemented.

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

Images