Ionized layer inversion method and system based on space-ground multi-source data fusion

Abstract

The invention provides an ionosphere inversion method and system based on space-ground multi-source data fusion, and belongs to the technical field of ionosphere inversion. The problems that an existing ionosphere inversion method is poor in precision, low in timeliness and poor in adaptability to ionosphere change characteristics in different regions and under different conditions are solved. According to the method, after a proper ionized layer model is selected, sky-ground data are acquired and processed, and the critical frequency of an ionized layer, the vertical height at which the electron density reaches the maximum value and the total electron content of the ionized layer are reconstructed by using an improved Kriging interpolation method; and based on the reconstruction result, analyzing the change rule of the ionized layer characteristics along with time and space distribution and under different disturbance conditions. The method can improve the precision and reliability of ionosphere inversion, so that key parameters such as electron density distribution, critical frequency, total electron content and the like of the ionosphere are described more accurately, and more accurate data support and theoretical guidance are provided for the fields of navigation positioning, communication system optimization and space weather monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of ionospheric inversion, and more particularly, to an ionospheric inversion method and system based on the fusion of space-based and ground-based multi-source data. Background Art

[0002] The formation of the ionosphere is due to the action of solar radiation (especially ultraviolet rays and X-rays) on neutral atmospheric molecules and atoms, resulting in gas ionization. The ionosphere can reflect and refract high-frequency radio waves, enabling radio communication to cover longer distances and playing an important role in military communication and oceanic communication. The study of the ionosphere can not only predict communication interruptions, optimize wireless communication networks, improve the reliability and stability of military high-frequency communication, but also improve navigation and positioning accuracy, support earthquake monitoring and natural disaster early warning, enhance the anti-jamming ability of satellite communication, and provide a scientific basis for space weather forecasting and deep space exploration missions. However, due to the ionosphere being easily affected by solar activity, geomagnetic activity, seasons, and diurnal cycles, and having obvious spatio-temporal variation characteristics, how to obtain high-precision ionospheric characteristic parameters (such as electron density profiles, etc.) has become the focus of research in the electromagnetic field at home and abroad.

[0003] Given the extremely strong spatio-temporal characteristics of the ionosphere, it is difficult to comprehensively capture these changes using a single observation method. Therefore, the method of fusing space-based and ground-based data for ionospheric inversion has received great attention. Ground-based equipment mainly provides vertical electron density data (such as TEC), while space-based equipment can obtain a wider range of ionospheric information, including vertical profiles of electron density, electron temperature, and ion density, covering global observation data. Complementary use of space-based observations (such as satellite occultation technology) and ground-based observation data (such as GNSS, ground ionospheric sounders) can achieve higher resolution in space and time and obtain more accurate ionospheric characteristics.

[0004] In recent years, many scholars have conducted extensive research on the fusion of space-based and ground-based ionospheric data. For example, Mitchell et al. reconstructed global time-varying three-dimensional ionospheric electron density information by fusing various space-based and ground-based ionospheric measurement data, Joshua Semeter et al. proposed a mathematical framework for fusing the total electron content measured by incoherent scatter radar (ISR) and global navigation satellite system (GNSS), and Wang Yang et al. analyzed the three-dimensional evolution characteristics of the ionosphere during magnetic storms after fusing GNSS and COSMIC-2 data. However, the above research needs to be improved in terms of data fusion accuracy and timeliness, and the adaptability to ionospheric change characteristics in different regions and under different conditions is not strong enough. Summary of the Invention

[0005] The technical problems to be solved by the present invention are:

[0006] To solve the problems existing in the existing ionospheric inversion methods, such as poor accuracy, low timeliness, and poor adaptability to the ionospheric variation characteristics in different regions and under different conditions.

[0007] The technical solution adopted by the present invention to solve the above technical problems:

[0008] The present invention provides an ionospheric inversion method for integrating space-based and ground-based multi-source data, including the following steps:

[0009] S100. According to different requirements, select a suitable ionospheric model, and the models include the IRI model, the NeQuick model, and the TIE-GCM model;

[0010] S200. Obtain space-based and ground-based data and perform data processing, including data acquisition and data processing;

[0011] S300. Use an improved Kriging interpolation method to reconstruct the critical frequency of the ionosphere, the vertical height at which the electron density reaches the maximum value, and the total electron content of the ionosphere;

[0012] S400. Based on the reconstruction results, analyze the variation laws of ionospheric characteristics with time, space distribution, and under different perturbation conditions.

[0013] Further, in step S100, when long-term statistical analysis is required, the IRI model is selected; when facing GNSS navigation and ionospheric correction requirements, the NeQuick model is selected; when conducting high-precision scientific research and space weather prediction, the TIE-GCM model is selected.

[0014] Further, in step S200, it includes,

[0015] S210. Data acquisition, obtain ground-based data from GNSS stations, ground radars, antenna arrays, and space-based data from satellite missions, including ground-based data covering the target area and time period and space-based data containing complete metadata;

[0016] S220. Data processing, perform data screening on space-based data and ground-based data, remove invalid data, and screen data within the target frequency band or area; unify the ground-based data into the WGS84 or target projection coordinate system, convert the orbital parameters of the space-based data into the geocentric coordinate system or geographic coordinate system; then perform time synchronization to unify the GPS time and UTC time to the same time reference; finally, perform data correction, including correcting system errors and correcting environmental interference, removing the bias of the receiver, and correcting the multipath effect and electromagnetic interference.

[0017] Further, in step S300, it includes,

[0018] S310. Semi-variogram modeling. Calculate the semi-variogram of the observed data obtained in step S200 using the following formula, and fit the semi-variogram model:

[0019]

[0020] where γ(h) is the estimated value of the semi-variogram with a separation distance of h, S(h) is the number of pairs of all points with a separation distance of h, Z(θ i ) is the average density of the sample point θ i , and Z(θ i + h) is the average density of the sample point θ i + h;

[0021] S320. Interpolation calculation. Calculate the distance weights using the following formula. The weight is proportional to the reciprocal of the distance, and the closer the known point, the higher the weight;

[0022]

[0023] where d i is the distance between the unknown point and the known point x i ; p is the power parameter; w i is the weight of the known point x i ;

[0024] Use the weighted average formula for interpolation calculation to predict the critical frequency of the unknown point, the vertical height at which the electron density reaches the maximum value, and the total electron content of the ionosphere:

[0025]

[0026] where is the estimated value of the unknown point, x i is the value of the known point. To ensure an unbiased estimate,

[0027] S330. The reconstructed objective function J is:

[0028] J = ||f 0 F 2,bc (r, t) - f 0 F 2,mo (r, t)|| + … + ||h m F 2,bc (r, t) - h m F 2,mo (r, t)|| + … + ||C TEC,bc (r, t) - D TEC,mo (r, t)||

[0029] where f 0 F 2,flag 、hm F 2,flag 、D TEC,flag respectively represent the critical frequency of the observation or background model, the vertical height at which the electron density reaches its maximum, and the total electron content of the ionosphere. The flag is represented by bc or mo, indicating the observed value or the background model value respectively; (r,t) represents spatial and temporal variations.

[0030] Furthermore, the general indicators of the inversion process are as follows:

[0031] 1) Horizontal range: 90°S - 90°N, I80°W - 180°E;

[0032] 2) Horizontal resolution: not greater than 500km * 1000km;

[0033] 3) Altitude range: 60km - 36000km;

[0034] 4) Temporal frequency of the three - dimensional electron density prediction field product: 15 minutes;

[0035] 5) Prediction duration of the three - dimensional electron density prediction field product: 2 days;

[0036] 6) Peak electron density accuracy system deviation of the three - dimensional electron density near - real - time fusion field (during quiet space environment): not greater than 15%.

[0037] An ionospheric inversion system for space - ground multi - source data fusion, which has program modules corresponding to the above steps and executes the steps in the ionospheric inversion method for space - ground multi - source data fusion when running.

[0038] An ionospheric inversion method for space - ground multi - source data fusion, comprising the following steps:

[0039] S100. Select the IRI ionospheric model;

[0040] S200. Obtain ground - based data from ground vertical sounding stations, GNSS receiving stations, satellite beacon receiving stations, and space - based data from COSMIC constellation occultation receivers. Process the above data, remove extreme outliers, and screen the data within the target frequency band or region; Set the ground GNSS stations and COSMIC satellites to receive the signals of GPS satellites and Beidou satellites, and the satellite beacon receivers to receive the COSMIC three - band satellite beacon signals, and unify the ground - based data into the WGS84 format;

[0041] S300. Use the improved Kriging interpolation method to reconstruct the critical frequency of the ionosphere, the vertical height at which the electron density reaches its maximum, and the total electron content of the ionosphere;

[0042] First, use the measurements of multiple vertical sounding stations to reconstruct the critical frequency of the ionosphere and the vertical height at which the electron density reaches its maximum. The specific steps are as follows:

[0043] S310. Calculate the Z observed by the vertical sounder 垂测仪 and the increment of the Z output value of the IRI model IRI at the same moment and position is:

[0044] ΔZ = Z 垂测仪 - Z IRI

[0045] Define the distance of the ionosphere as:

[0046]

[0047] where x a and y a are the precision and latitude of point a respectively; x b and y b are the precision and latitude of point b respectively; F is the scale factor;

[0048] S320. Calculate the increment of the ionospheric eigenvalue:

[0049]

[0050] where W i represents the distance weight at the i-th point (x i , y i ), and ΔZ i represents the increment of the ionospheric eigenvalue at the i-th point (x i , y i ); assume that the observation data at the same moment contains N points;

[0051] S320. Solve the weighting coefficient:

[0052] ∑V ij W ij = V j0 - μ

[0053] where V ij represents the correlation distance between the i-th point and the j-th point, and μ is the Lagrange multiplier,

[0054] S330. The reconstructed f 0 F 2 and h m F 2 are distributed as:

[0055] Z i = Z IRI,i + ΔZ 0,i

[0056] The total electron content of the ionosphere reconstructed based on the discretization inversion theory is given by the following formula:

[0057]

[0058] where the vector k is composed of the observation data D STE from ground-based GPS, occultation, and satellite beacon receivers; C is the intercept matrix of the signal propagation path in the discretized grid; H is the distribution of the unknown electron density; e is the error introduced by measurement and discretization; G is the number of paths of ground-based GNSS observations; R is the number of paths of LEO occultation observations; B is the number of observation paths of satellite beacons; and N is the number of grids;

[0059] The formula for solving the electron density is:

[0060]

[0061] where and are the electron densities of the j-th grid at the k-th and (k + 1)-th iterations, respectively; i is the STEC path number; j is the grid number; q i is the total electron content of the i-th path; a ij is the projection of the i-th path within the j-th grid; ||a i || is the total length of the i-th path; and ρ is the relaxation factor;

[0062] S400. Based on the reconstruction results, analyze the variation laws of ionospheric characteristics with time, spatial distribution, and different perturbation conditions.

[0063] Furthermore, the objective function of data fusion inversion is:

[0064] J = ||f 0 F 2,bc (r, t) - f 0 F 2,mo (r, t)|| + … + ||h m F 2,bc (r, t) - h m F 2,mo (r, t)|| + … + ||C TEC,bc (r, t) - D TEC,mo (r, t)||

[0065] where f 0 F 2,flag , h m F 2,flag , D TEC,flagThey respectively represent the critical frequency of the observation or background model, the vertical height at which the electron density reaches the maximum value, and the total electron content of the ionosphere. The flag is represented by bc or mo, indicating the observed value or the background model value respectively; (r, t) represents the spatial and temporal variations.

[0066] An ionospheric inversion system for integrating multi-source data from space and ground, which has program modules corresponding to the steps of the above-mentioned claim 7 or 8, and executes the steps in the above-mentioned ionospheric inversion method for integrating multi-source data from space and ground when running.

[0067] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0068] For an ionospheric inversion method and system for integrating multi-source data from space and ground of the present invention, after selecting a suitable ionospheric model, space and ground data are acquired and processed, and the critical frequency of the ionosphere, the vertical height at which the electron density reaches the maximum value, and the total electron content of the ionosphere are reconstructed by using an improved Kriging interpolation method; based on the reconstruction results, the variation laws of the ionospheric characteristics with time, space distribution, and different perturbation conditions are analyzed. The improved Kriging interpolation method calculates the semi-variogram of the observed data and selects a suitable semi-variogram model (such as spherical model, exponential model) for fitting, which helps to analyze the spatial autocorrelation of the data and provides more accurate weights for interpolation. The above operations improve the accuracy and reliability of ionospheric inversion, so as to be able to more accurately characterize the key parameters such as the electron density distribution, critical frequency, and total electron content of the ionosphere, and provide more accurate data support and theoretical guidance for the fields of navigation and positioning, communication system optimization, and space weather monitoring. Description of the Drawings

[0069] Figure 1 It is a flowchart of an ionospheric inversion method for integrating multi-source data from space and ground in an embodiment of the present invention;

[0070] Figure 2 It is a statistical chart of the ionospheric electron density errors at all grid points before and after multi-source data fusion in an embodiment of the present invention. Detailed Embodiments

[0071] To make the above-mentioned objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be given with reference to the accompanying drawings.

[0072] Specific Embodiment 1: Combining Figure 1 As shown, the present invention provides an ionospheric inversion method for integrating multi-source data from space and ground, including the following steps:

[0073] S100. Select a suitable ionospheric model according to different requirements;

[0074] The optional models include the IRI model, the NeQuick model, and the TIE-GCM model, but are not limited to these models;

[0075] When meeting the following different requirements, the specific principles are as follows: When long-term statistical analysis is required, the IRI model is selected. This model is applicable globally, has high accuracy, and efficient calculation, but has insufficient description of dynamic changes; When facing GNSS navigation and ionospheric correction requirements, the NeQuick model is selected, which can quickly calculate TEC and has strong real-time performance, but has relatively low accuracy in the polar regions and limited physical interpretation; When conducting high-precision scientific research and space weather prediction, the TIE-GCM model can be selected, which has a strong physical basis and can simulate dynamic processes, but has high computational complexity and requires a large amount of input data;

[0076] S200. Obtain space-based and ground-based data and perform data processing, including data acquisition and data processing.

[0077] S210. Data acquisition, obtain ground-based data from GNSS stations, ground radars, and antenna arrays, and space-based data from satellite missions. The ground-based data is in the standard file formats of RINEX, NetCDF, and HDF, and the space-based data is in binary or text format. The text format includes SP3, IONEX, or other common formats; Obtain ground-based data covering the target area and time period and space-based data containing complete metadata. When selecting space-based data, the spatial resolution and time interval of space-based observations need to be considered; The spatial resolution determines the level of detail of the observed data. High spatial resolution can provide more detailed surface information, thus supporting precise analysis of local area changes or features; The time interval affects the timeliness and continuity of the data. A shorter time interval can capture rapidly changing phenomena in a timely manner, such as weather changes and environmental dynamics, ensuring the real-time performance and continuous tracking ability of the data; The combination of the two can ensure the accuracy of the data while ensuring the ability to reflect the changes of the target in real time, meeting the needs of different research or applications.

[0078] S220. Data processing, screen the space-based data and ground-based data to remove invalid data (such as extreme outliers), and screen the data within the target frequency band or region; Unify the ground-based data into the WGS84 or target projection coordinate system, and convert the orbital parameters of the space-based data into the Earth-centered coordinate system (ECEF) or geographical coordinate system (latitude and longitude); Perform time synchronization to unify the GPS time and UTC time to the same time reference, ensuring the consistency of the time interval of the processed data; Perform data correction, including correcting systematic errors and environmental interference, removing the biases of the receivers, and correcting multipath effects and electromagnetic interference problems.

[0079] S300. Reconstruct the critical frequency of the ionosphere, the vertical height at which the electron density reaches its maximum, and the total electron content of the ionosphere using an improved Kriging interpolation method. Specifically,

[0080] S310. Semivariogram modeling. Calculate the semivariogram of the observed data using the following formula to analyze the spatial autocorrelation of the data; select an appropriate semivariogram model (such as the spherical model or the exponential model) to fit the semivariogram model:

[0081]

[0082] where γ(h) is the estimated value of the semivariogram at a separation distance of h, S(h) is the number of pairs of all points at a separation distance of h, Z(θ i ) is the average density of the sample point θ i , and Z(θ i + h) is the average density of the sample point θ i + h;

[0083] During the process of semivariogram modeling, attention needs to be paid to the three parameters of the semivariogram graph: the range, which reflects the size of the influence range of the regionalized variable; the nugget effect, which reflects the possible degree of internal randomness of the regionalized variable; the sill, which reflects the magnitude of the variable change; the three parameters of the semivariogram graph (nugget effect, range, sill) are crucial for fitting the semivariogram model. They not only determine the shape of the semivariogram but also directly affect the fitting effect and prediction accuracy of the model;

[0084] S320. Interpolation calculation. Calculate the distance weights using the following formula. The weight is proportional to the reciprocal of the distance, and the closer the known point, the higher the weight;

[0085]

[0086] where d i is the distance between the unknown point and the known point x i ; p is the power parameter, usually taking a value of 2; w i is the weight of the known point x i ;

[0087] Use the weighted average formula for interpolation calculation to predict the critical frequency of the unknown point, the vertical height at which the electron density reaches its maximum, and the total electron content of the ionosphere:

[0088]

[0089] where, is the estimated value of the unknown point, x i is the value of the known point. To ensure an unbiased estimate, where,

[0090] In S330, the reconstructed objective function J is as follows:

[0091] J = ||f 0 F 2,bc (r, t) - f 0 F 2,mo (r, t)|| + … + ||h m F 2,bc (r, t) - h m F 2,mo (r, t)|| + … + ||C TEC,bc (r, t) - D TEC,mo (r, t)||

[0092] Among them, f 0 F 2,flag , h m F 2,flag , D TEC,flag respectively represent the critical frequency of the observation or background model, the vertical height at which the electron density reaches the maximum value, and the total electron content of the ionosphere. flag is represented by bc or mo, indicating the observed value or the background model value respectively; (r, t) represents the spatial and temporal variations;

[0093] In S400, based on the reconstruction results, analyze the variation laws of the ionospheric characteristics with time, space distribution, and different perturbation conditions, including

[0094] Consider the time distribution of the ionospheric characteristics, including period variation, diurnal variation, annual variation, and seasonal variation; at the same time, consider the spatial distribution of the ionospheric characteristics, including latitude variation; by analyzing the variation of the electron density error after the inversion of the ionospheric fusion, further verify the effectiveness and accuracy of the proposed method.

[0095] The general indicators of the inversion process applicable to the present invention are as follows:

[0096] 1) Horizontal range: 90°S - 90°N, I80°W - 180°E;

[0097] 2) Horizontal resolution: not greater than 500 km * 1000 km;

[0098] 3) Altitude range: 60 km - 36000 km;

[0099] 4) Time frequency of the three-dimensional electron density prediction field product: 15 minutes;

[0100] 5) Prediction duration of the three-dimensional electron density prediction field product: 2 days;

[0101] 6) Peak electron density precision systematic deviation of the three-dimensional electron density near-real-time fusion field (during the quiet period of the space environment): not greater than 15%.

[0102] Specific Embodiment 2: The present invention provides an ionospheric inversion system for space-ground multi-source data fusion, which system has program modules corresponding to the above steps, and when running, executes the steps in the ionospheric inversion method for space-ground multi-source data fusion as described above.

[0103] Other combinations and connection relationships in this embodiment are the same as those in Specific Embodiment 1.

[0104] Specific Embodiment 3: As shown in Figure 1 The present invention provides an ionospheric inversion method for space-ground multi-source data fusion, including the following steps:

[0105] S100: Since long-term statistical analysis is required, the IRI ionospheric model is selected;

[0106] S200: Obtain ground-based data from ground sounding stations, GNSS receiving stations, satellite beacon receiving stations, and space-based data from occultation receivers of the COSMIC constellation (composed of 6 low-Earth orbit LEO satellites with an inclination of 72°). Process these data to remove invalid data such as extreme outliers, and screen the data within the target frequency band or region; it is set that ground GNSS stations and COSMIC satellites can receive signals including GPS satellites and "Beidou" satellites, and satellite beacon receivers can receive COSMIC three-band satellite beacon signals, and unify the ground-based data into the WGS84 format; considering the limited computing resources of a single computer, the horizontal range is delimited as: 20°N - 90°N, 100°W - 180°E; the altitude range: 60 km - 36000 km; data fusion is performed every 15 minutes; the latitude interval is set to 10°, the longitude interval is 18°, the altitude interval below 10000 km is 50 km, and the altitude interval above 10000 km is 200 km.

[0107] S300: Use the improved Kriging interpolation method to reconstruct the critical frequency of the ionosphere, the vertical height at which the electron density reaches the maximum value, and the total electron content of the ionosphere;

[0108] First, use the measurements of multiple sounding stations to reconstruct the critical frequency of the ionosphere and the vertical height at which the electron density reaches the maximum value. The specific steps are as follows:

[0109] S310: Calculate the increment of Z 垂测仪 observed by the sounder and the output value Z IRI of the IRI model at the same moment and position as:

[0110] ΔZ = Z 垂测仪 -Z IRI

[0111] Define the distance of the ionosphere as:

[0112]

[0113] Among them, x a and y a are the precision and latitude of point a (in degrees); x b and y b are the precision and latitude of point b, and F is the scale factor;

[0114] S320. Calculate the increment of the ionospheric eigenvalue:

[0115]

[0116] Among them, W i represents the distance weight at the i-th point (x i , y i ), and ΔZ i represents the increment of the ionospheric eigenvalue at the i-th point (x i , y i ); assume that the observation data at the same moment contains N points;

[0117] S320. Solve the weighting coefficient:

[0118] ∑V ij W ij = V j0 - μ

[0119] Among them, V ij represents the correlation distance between the i-th point and the j-th point, and μ is the Lagrange multiplier,

[0120] S330. The reconstructed f 0 F 2 and h m F 2 distributions are:

[0121] Z i = Z IRI,i + ΔZ 0,i

[0122] The problem of reconstructing the total electron content of the ionosphere based on the discretized inversion theory can be expressed as the following formula:

[0123]

[0124] Among them, the vector k is composed of the observation data D of ground-based GPS, occultation, and satellite beacon receivers STECComposition; C is the intercept matrix of the signal propagation path in the discretized grid; H is the distribution of the unknown electron density; e is the error introduced by the measurement and discretization; G is the number of paths of ground-based GNSS observations; R is the number of paths of LEO occultation observations; B is the number of paths of satellite beacon observations; N is the number of grids;

[0125] The electron density solution formula is:

[0126]

[0127] Where, and are the electron densities of the j-th grid at the k-th and (k + 1)-th iterations respectively; i is the STEC path number; j is the grid number; q i is the total electron content of the i-th path; a ij is the projection of the i-th path within the j grids; ||a i || is the total length of the i-th path; ρ is the relaxation factor, varying between 0 and 1;

[0128] The objective function for reconstruction is:

[0129] J = ||f 0 F 2,bc (r, t) - f 0 F 2,mo (r, t)|| + … + ||h m F 2,bc (r, t) - h m F 2,mo (r, t)|| + … + ||C TEC,bc (r, t) - D TEC,mo (r, t)||

[0130] Where, f 0 F 2,flag , h m F 2,flag , D TEC,flag represent the critical frequency of the observation or background model, the vertical height at which the electron density reaches its maximum, and the total electron content of the ionosphere respectively. flag is represented by bc or mo, indicating the observed value or the background model value respectively; (r, t) represents the spatial and temporal variations;

[0131] S400. Based on the reconstruction results, analyze the variation laws of the ionospheric characteristics with time, spatial distribution, and different perturbation conditions.

[0132] Define the fused electron density error as ΔN e , ΔN e = N b - N v where, N bRepresents the electron density before or after fusion, N v Represents the true electron density. By analyzing the change in the error of the ionospheric fused electron density after inversion, as Figure 2 shown, calculate the absolute average value of ΔN e before and after fusion respectively: it is 1.39×10 11 el.m -3 before fusion, and it decreases to 0.7×10 11 el.m -3 after fusion; while the root mean square error before fusion is 0.62×10 11 el.m -3 and the root mean square error after fusion is 0.37×10 11 el.m -3 . It can be seen that the error of the electron density decreases significantly after fusion, further verifying the effectiveness and accuracy of the proposed method.

[0133] Specific implementation plan four: The present invention provides an ionospheric inversion system for space-ground multi-source data fusion, which has program modules corresponding to the above steps and executes the steps in the ionospheric inversion method for space-ground multi-source data fusion described above when running.

[0134] The other combinations and connection relationships in this implementation plan are the same as those in specific implementation plan three.

[0135] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art of the present invention can make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will all fall within the protection scope of the present invention.

Claims

1. An ionospheric inversion method based on multi-source data fusion of ground-based and space-based data, characterized in that: The following steps are involved: S100, selecting a suitable ionospheric model according to different requirements, wherein the model includes an IRI model, a NeQuick model and a TIE-GCM model; S200, acquiring earth-ground data and performing data processing, including data acquisition and data processing; S300, using the improved Kriging interpolation method to reconstruct the critical frequency of the ionosphere, the vertical height at which the electron density reaches the maximum value, and the total electron content of the ionosphere; S400. Based on the reconstruction results, analyze the variation patterns of ionospheric characteristics with time, spatial distribution and under different disturbance conditions.

2. The ionospheric inversion method of ground-to-earth multi-source data fusion according to claim 1, characterized in that: In step S100, when long-term statistical analysis is required, the IRI model is selected; when facing GNSS navigation and ionospheric correction requirements, the NeQuick model is selected; when conducting high-precision scientific research and space weather forecasting, the TIE-GCM model is selected.

3. The ionospheric inversion method of ground-to-earth multi-source data fusion according to claim 2, characterized in that: In step S200, it includes: S210, data acquisition, obtaining ground-based data from GNSS sites, ground radars, antenna arrays, and space-based data from satellite missions, including ground-based data covering the target area and time period and space-based data containing complete metadata; S220, data processing, screen the space-based data and ground-based data, remove invalid data, and screen the data in the target frequency band or area; unify the ground-based data into WGS84 or the target projection coordinate system, and convert the orbit parameters of the space-based data into the geocentric coordinate system or the geographic coordinate system; then perform time synchronization to unify the GPS time and UTC time as the time base; finally, perform data correction, including correcting system errors and correcting environmental interference, removing the receiver's deviation, and correcting multipath effects and electromagnetic interference.

4. The ionospheric inversion method of ground-to-earth multi-source data fusion according to claim 3, characterized in that: In step S300, it includes: S310, semivariogram modeling, using the following formula to calculate the semivariogram of the observation data obtained in step S200, and fitting the semivariogram model: where γ(h) is the estimated value of the semivariogram with a distance h between them, S(h) is the number of pairs of all points with a distance h between them, and Z(θ i ) is the sample point θ i The average density, Z(θ i +h) is the sample point θ i +h average density; S320, interpolation calculation, using the following formula to calculate the distance weight, the weight is proportional to the inverse of the distance, the closer the distance is, the higher the weight is; Among them, d i is the unknown point and the known point x i The distance between them; p is the power parameter; w i is a known point x i The weight of The weighted average formula is used for interpolation to predict the critical frequency of the unknown point, the vertical height at which the electron density reaches its maximum value, and the total electron content of the ionosphere: in, is the estimated value of the unknown point, x i is the value of the known point. In order to ensure unbiased estimation, S330, the reconstructed objective function J is: J=||f0F 2,bc (r,t)-f0F 2,mo (r,t)||+…+||h m F 2,bc (r,t)-h m F 2,mo (r,t)||+… +||C TEC,bc (r,t)-D TEC,mo (r,t)|| Among them, f0F 2,flag 、h m F 2,flag , D TEC,flag They represent the critical frequency of observation or background model, the vertical height where the electron density reaches the maximum value, and the total electron content of the ionosphere respectively. Flag is represented by bc or mo, which represents the observation value or the background model value respectively; r, t0 represent spatial and temporal changes.

5. The ionospheric inversion method of ground-to-earth multi-source data fusion according to claim 4, characterized in that: The general indicators of the inversion process are as follows: 1) Horizontal range: 90°S-90°N, 180°W-180°E; 2) Horizontal resolution: no more than 500km*1000km; 3) Altitude range: 60km-36000km; 4) Time frequency of three-dimensional electron density forecast field products: 15 minutes; 5) Forecast duration of three-dimensional electron density forecast field product: 2 days; 6) System deviation of peak electron density accuracy of near real-time fusion field of three-dimensional electron density (quiet period of space environment): no more than 15%.

6. An ionospheric inversion system with ground-to-ground multi-source data fusion, characterized by: The system has a program module corresponding to the steps of any one of claims 1 to 5, and executes the steps in the above-mentioned ionospheric inversion method of ground-to-space multi-source data fusion when running.

7. An ionospheric inversion method based on ground-to-earth multi-source data fusion, characterized in that: The following steps are involved: S100, select IRI ionosphere model; S200, obtaining ground-based data from ground vertical survey stations, GNSS receiving stations, satellite beacon receiving stations, and celestial data from COSMIC constellation occultation reception, processing the above data, removing extreme outliers, and screening data in a target frequency band or region; Set the ground GNSS station and COSMIC satellite to receive signals from GPS satellites and Beidou satellites, and the satellite beacon receiver to receive COSMIC three-band satellite beacon signals, and unify the ground-based data into WGS84 format; S300, using the improved Kriging interpolation method to reconstruct the critical frequency of the ionosphere, the vertical height at which the electron density reaches the maximum value, and the total electron content of the ionosphere; First, the critical frequency of the ionosphere and the vertical height at which the electron density reaches the maximum value are reconstructed using measurements from multiple vertical measurement stations. The specific steps include: S310, calculate the Z of the plumb line instrument observation 垂测仪 and the IRI model output value Z IRI At the same time, the increment of position is: ΔZ=Z 垂测仪 -Z IRI The distance to the ionosphere is defined as: Among them, x a and a are the accuracy and latitude of point a respectively; x b and b are the precision and latitude of point b respectively; F is the scale factor; S320, calculating the ionospheric characteristic value increment: Among them, W i It represents the i-th point (x i ,y i Distance weight at 0, ΔZ i It represents the i-th point (x i ,y i ) point; Assume that the observation data at the same time contains N points; S320, solving the weighted coefficient: Among them, V ij represents the relative distance between the i-th point and the j-th point, μ is the Lagrange multiplier, S330, reconstructed f0F2 and h m The F2 distribution is: With i =Z IRI,i +ΔZ 0,i The total electron content of the ionosphere reconstructed based on the discretization inversion theory is as follows: The vector k is composed of observation data D from ground-based GPS, occultation and satellite beacon receivers. STEC composition; C is the intercept matrix of the signal propagation path in the discretized grid; H is the distribution of the unknown electron density; e is the error introduced after measurement and discretization; G is the number of paths for ground-based GNSS observations; R is the number of paths for LEO occultation observations; B is the number of observation paths for satellite beacons; N is the number of grids; The electron density solution formula is: in, and are the electron density of the jth grid at iterations k and k+1, respectively; i is the STEC path number; j is the grid number; q i is the total electron content of the ith path; a ij is the projection of the i-th path in the j-grid; ||a i || is the total length of the i-th path; ρ is the relaxation factor; S400. Based on the reconstruction results, analyze the variation patterns of ionospheric characteristics with time, spatial distribution and under different disturbance conditions.

8. The ionospheric inversion method of ground-to-earth multi-source data fusion according to claim 7, characterized in that: The objective function of data fusion inversion is: J=||f0F 2,bc (r,t)-f0F 2,mo (r,t)||+…+||h m F 2,bc (r,t)-h m F 2,mo (r,t)||+…+||C TEC,bc (r,t)-D TEC,mo (r,t)|| Among them, f0F 2,flag 、h m F 2,flag , D TEC,flag They represent the critical frequency of observation or background model, the vertical height where the electron density reaches the maximum value, and the total electron content of the ionosphere respectively. Flag is represented by bc or mo, which represents the observation value or the background model value respectively; (r, t) represents the spatial and temporal changes.

9. An ionospheric inversion system with ground-to-ground multi-source data fusion, characterized by: The system has a program module corresponding to the steps of claim 7 or 8, and executes the steps of the above-mentioned ionospheric inversion method of ground-to-space multi-source data fusion when running.

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