Global ionospheric TEC estimation method and device considering time and space constraints

Abstract

The present disclosure provides a global ionospheric TEC estimation method and device considering the temporal and spatial constraint relationship, including: firstly obtaining a GNSS observation file, constructing a geometry-free combination of pseudorange and phase based on the GNSS observation file; and solving it based on the phase smoothed pseudorange method to obtain an ionospheric STEC data set; secondly, constructing a plurality of experimental data sets corresponding to preset constraint scenarios according to the ionospheric STEC data set; then determining the scenario constraint information corresponding to each preset constraint scenario based on the experimental data set, and determining the target constraint information based on the scenario constraint information corresponding to the preset constraint scenario; finally, calculating a DCB value based on a DCB calculation formula according to the target constraint information and the experimental data set, and calculating VTEC data based on the DCB value, the experimental data set and the VTEC calculation formula. The present disclosure ensures the temporal and spatial consistency of the ionospheric TEC estimation result.

Description

Technical Field

[0001] The present disclosure relates to the field of ionospheric science and technology, and in particular to a global ionospheric TEC estimation method and device taking into account time and space constraints. Background Art

[0002] As an important component of the Earth's near-Earth space, the ionosphere covers an area of ​​about 60 to 1000 kilometers above the Earth's surface and is rich in free electrons and ions. It is not only the core transmission medium of the satellite-to-ground communication link, but its dynamic changes have a significant impact on the performance and stability of radio systems such as shortwave communication, satellite communication and satellite navigation. Therefore, accurate estimation of key parameters of the ionosphere, especially the total electron content (TEC), is crucial to optimizing the design of communication systems and ensuring their stable operation in complex space environments. Accurate acquisition of TEC can not only improve the positioning accuracy of the global navigation satellite system (GNSS), but also help study the dynamic changes of the ionosphere and predict space weather events, thereby enhancing the safety and reliability of global communication systems.

[0003] In the related technologies, the measurement of ionospheric total electron content (TEC) based on densely arranged GNSS receivers on the ground has made significant progress and has become the main measurement method. However, hardware delay (including the daily variation bias DCB of the station and the DCB of the satellite) is still the main source of error in TEC measurement. The current TEC estimation method usually assumes that the hardware delay between the GNSS satellite and the receiver remains unchanged within a certain time window, and relies on the ionospheric model to describe the vertical ionospheric total electron content (VTEC). The corresponding methods include solving the hardware delay using nighttime TEC data and assuming that the VTEC above the grid points within a certain time and space range is equal. However, the accurate estimation of global ionospheric TEC has not been fully solved, which limits the effectiveness of monitoring and prediction. At the same time, the problem of temporal and spatial consistency of VTEC still exists in the observation of multiple systems and multiple stations. In addition, the related technologies lack effective temporal and spatial constraints, and fail to fully consider the relationship between satellites and constellations, stations and time, etc., resulting in large errors in the estimation results. Summary of the invention

[0004] The disclosed embodiments at least provide a global ionospheric TEC estimation method and device that considers the temporal and spatial constraint relationships, fully considers the constraint information between various preset constraint scenarios, estimates the DCB parameters and ionospheric VTEC according to the constraint information and the test data set through the DCB calculation formula and the VTEC calculation formula, and ensures the consistency of the ionospheric TEC estimation results in time and space.

[0005] The present disclosure provides a method for estimating global ionospheric TEC taking into account time and space constraints, including:

[0006] Acquire a GNSS observation file, construct a geometry-free combination of pseudorange and phase based on the GNSS observation file; and solve the geometry-free combination based on a phase smoothed pseudorange method to obtain an ionospheric STEC data set;

[0007] According to the ionospheric STEC data set, construct a plurality of test data sets corresponding to preset constraint scenarios respectively; determine the scenario constraint information corresponding to each of the preset constraint scenarios based on the test data sets, and determine the target constraint information based on the scenario constraint information corresponding to the plurality of preset constraint scenarios;

[0008] A DCB value is calculated based on the target constraint information and the test data set based on a DCB calculation formula, and VTEC data is calculated based on the DCB value, the test data set and a VTEC calculation formula.

[0009] The embodiment of the present disclosure provides a global ionospheric TEC estimation device considering time and space constraints, including:

[0010] A data solving module is used to obtain a GNSS observation file, construct a geometry-free combination of pseudorange and phase based on the GNSS observation file; and solve the geometry-free combination based on a phase smoothed pseudorange method to obtain an ionospheric STEC data set;

[0011] A constraint determination module is used to construct a plurality of test data sets corresponding to preset constraint scenarios according to the ionospheric STEC data set; determine the scenario constraint information corresponding to each of the preset constraint scenarios based on the test data sets, and determine the target constraint information based on the scenario constraint information corresponding to the preset constraint scenarios;

[0012] The data calculation module is used to calculate a DCB value based on a DCB calculation formula according to the target constraint information and the test data set, and to calculate VTEC data based on the DCB value, the test data set and the VTEC calculation formula.

[0013] An embodiment of the present disclosure provides a computer device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, a global ionospheric TEC estimation method considering spatiotemporal constraints as described in any possible implementation manner described above is performed.

[0014] An embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for estimating the global ionospheric TEC taking into account the spatiotemporal constraints as described in any of the above possible implementations is implemented.

[0015] The global ionospheric TEC estimation method and device considering the spatiotemporal constraint relationship provided in the embodiments of the present disclosure fully consider the spatiotemporal relationship between the various preset constraint scenarios by constructing data sets corresponding to different preset constraint scenarios, and constructing constraint information corresponding to each scenario based on the data sets of different scenarios. Subsequently, the DCB parameters and ionospheric VTEC are estimated based on the constraint information and the experimental data set through the DCB calculation formula and the VTEC calculation formula. In this way, the consistency of the ionospheric TEC estimation results in time and space can be ensured.

[0016] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required to be cited in the embodiments. The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used together with the specification to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flow chart of a global ionospheric TEC estimation method considering time and space constraints provided by an embodiment of the present disclosure is shown;

[0019] Figure 2 A flowchart of a method for constructing an experimental data set in a global ionospheric TEC estimation method taking into account spatiotemporal constraints provided by an embodiment of the present disclosure is shown;

[0020] Figure 3 A flow chart of a method for determining target constraint information in a global ionospheric TEC estimation method considering spatiotemporal constraints provided by an embodiment of the present disclosure is shown;

[0021] Figure 4 A schematic diagram of the structure of a global ionospheric TEC estimation device considering time and space constraints provided by an embodiment of the present disclosure is shown;

[0022] Figure 5A schematic diagram of the structure of a computer device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the present disclosure for protection, but merely represents the selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present disclosure.

[0024] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0025] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0026] The ionosphere, as an important component of the Earth's near-Earth space, covers an area of ​​about 60 to 1000 kilometers above the Earth's surface and is rich in free electrons and ions. As the core transmission medium of satellite-to-ground communication links, disturbances in the ionosphere have a significant impact on the radio signals propagating through it. This impact is directly related to the performance and stability of various radio systems such as shortwave communications, satellite communications, and satellite navigation. Accurate estimation of key parameters of the ionosphere, especially the total electron content (TEC), plays a vital role in optimizing the design of these systems and ensuring their stable operation in complex space environments. Accurate acquisition of TEC parameters can not only improve GNSS positioning accuracy, but also has important significance for understanding ionospheric dynamics, predicting space weather events, and protecting the safety and reliability of global communication systems.

[0027] According to research, in the relevant technologies, the measurement of ionospheric total electron content (TEC) based on densely arranged GNSS receivers on the ground has made significant progress and has become the main measurement method. However, hardware delay (including the daily variation bias DCB of the station and the DCB of the satellite) is still the main source of error in TEC measurement. The current TEC estimation method usually assumes that the hardware delay between the GNSS satellite and the receiver remains unchanged within a certain time window, and relies on the ionospheric model to describe the vertical ionospheric total electron content (VTEC). The corresponding methods include solving the hardware delay using nighttime TEC data and assuming that the VTEC above the grid points within a certain time and space range is equal. However, the precise estimation of global ionospheric TEC has not been fully solved, which limits the effectiveness of monitoring and prediction. At the same time, in the observation of multiple systems and multiple stations, the problem of temporal and spatial consistency of VTEC still exists. In addition, the relevant technology lacks effective temporal and spatial constraints, and fails to fully consider the relationship between satellites and constellations, stations and time, etc., resulting in large errors in the estimation results.

[0028] Based on the above research, a global ionospheric TEC estimation method and device taking into account the spatiotemporal constraint relationship are provided in an embodiment of the present disclosure. First, a GNSS observation file is obtained, and a geometry-free combination of pseudorange and phase is constructed based on the GNSS observation file; and it is solved based on the phase smoothed pseudorange method to obtain an ionospheric STEC data set; secondly, according to the ionospheric STEC data set, a plurality of experimental data sets corresponding to preset constraint scenarios are respectively constructed; then, based on the experimental data set, the scenario constraint information corresponding to each preset constraint scenario is determined, and the target constraint information is determined based on the scenario constraint information corresponding to the preset constraint scenario; finally, according to the target constraint information and the experimental data set, a DCB value is calculated based on the DCB calculation formula, and based on the DCB value, the experimental data set and the VTEC calculation formula, the VTEC data is calculated.

[0029] In the disclosed embodiment, by constructing data sets corresponding to different preset constraint scenarios, and constructing constraint information corresponding to each scenario based on the data sets of different scenarios, the spatiotemporal relationship between each preset constraint scenario is fully considered, and then the DCB parameters and ionospheric VTEC are estimated based on the constraint information and the test data set through the DCB calculation formula and the VTEC calculation formula. In this way, the consistency of the ionospheric TEC estimation results in time and space can be ensured.

[0030] To facilitate understanding of this embodiment, the execution subject of the global ionospheric TEC estimation method considering the spatiotemporal constraint relationship provided by the embodiment of the present disclosure is first introduced in detail. The execution subject of the global ionospheric TEC estimation method considering the spatiotemporal constraint relationship provided by the embodiment of the present disclosure is a computer device. The computer device can be a server. Among them, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data and artificial intelligence platforms.

[0031] The global ionospheric TEC estimation method considering the temporal and spatial constraints provided by the embodiment of the present application is described in detail below in conjunction with the accompanying drawings. Figure 1 As shown, it is a flow chart of a global ionospheric TEC estimation method considering the temporal and spatial constraints provided by an embodiment of the present disclosure, and the method includes the following S101-S103:

[0032] S101, obtaining a GNSS observation file, constructing a geometry-free combination of pseudorange and phase based on the GNSS observation file; and solving the geometry-free combination based on a phase smoothed pseudorange method to obtain an ionospheric STEC data set.

[0033] It can be understood that the GNSS observation file is a record of the observation data of the Global Navigation Satellite System (GNSS), which usually contains various information of the satellite signal obtained from the GNSS receiver, including timestamp, satellite ID, pseudorange, phase observation value, signal quality and other information. Here, pseudorange refers to the estimated value of the distance from the GNSS receiver to the satellite. Due to the influence of factors such as signal propagation time, satellite clock error and receiver clock error, the pseudorange is not the actual physical distance, but a distance calculated based on the received signal and the known satellite position. Phase refers to the phase angle information of the GNSS signal waveform. In phase measurement, the receiver not only measures the time of arrival of the satellite signal, but also measures the waveform phase of the signal, so that higher accuracy can be achieved than pseudorange. The basic principle of phase measurement is to calculate the phase of the signal by comparing the phase difference between the received signal and the locally generated reference signal. After obtaining the pseudorange and phase information in the GNSS observation file, a geometry-free combination is constructed based on the pseudorange and phase. Here, geometry-free combination refers to the data combination analysis performed without considering the geometric influence when processing GNSS observation data. The phase-smoothed pseudorange method is then used to solve the geometry-free combination. This method uses phase observations to correct the errors in pseudorange measurements, thereby reducing the impact of random noise on pseudoranges. The basic idea is to use the high precision of phase measurements to smooth pseudorange observations.

[0034] Specifically, since the data obtained by solving the geometry-free combination using the phase smoothed pseudorange method may contain errors caused by the signal multipath effect (the receiver receives multiple signal paths from the same satellite), in order to weaken this error, an ephemeris file can be obtained, which contains the orbit information and status of the satellite at a specific time. This information can help to better understand the propagation characteristics of satellite signals, and then make more accurate corrections to the data. Next, based on the ephemeris file, the solution results of the geometry-free combination are analyzed, especially for the elevation angle data. To this end, an elevation angle threshold can be set as the boundary value of the preset range. Only when the elevation angle of the satellite is greater than this threshold, the relevant data is considered reliable and worth retaining; at the same time, the elevation angle data within the preset range, that is, less than the elevation angle threshold, is eliminated to avoid the inaccuracy of these low elevation angle data due to the multipath effect, and then the ionospheric STEC data set is obtained. The ionospheric STEC data set is an uncorrected data set.

[0035] It can be understood that the total electron content (TEC) refers to the total number of all free electrons contained per unit area in a specific area of ​​the ionosphere. TEC is a broad concept, usually used to describe the overall electron density state of the ionosphere. The slant total electron content (STEC) refers to the total electron content along the slant line of sight path from the satellite to the receiver. Since the path measured by STEC is slanted, it contains electrons from all layers of the ionosphere passing through this path. The vertical total electron content (VTEC) refers to the total electron content per unit area on the vertical path, indicating the electron content of the ionosphere in the vertical direction. VTEC is usually regarded as the benchmark measurement value of ionospheric electrons. It should be noted that TEC is not directly equal to the simple sum of STEC and VTEC, but evaluates the electron content of the ionosphere through different measurement paths (slant and vertical). The global ionospheric TEC estimation method considering the temporal and spatial constraints proposed in the present disclosure is to approximately estimate the TEC value by calculating the value of VTEC.

[0036] S102, constructing a plurality of experimental data sets corresponding to preset constraint scenarios respectively according to the ionospheric STEC data set; determining the scenario constraint information corresponding to each of the preset constraint scenarios based on the experimental data sets, and determining the target constraint information based on the scenario constraint information corresponding to the plurality of preset constraint scenarios.

[0037] For example, in order to deeply understand the characteristics of the ionosphere and its impact on signal propagation under different constraint scenarios, the embodiments of the present disclosure propose to construct test data sets corresponding to different constraint scenarios. In the present disclosure, the preset constraint scenarios may include satellite constraint scenarios, constellation constraint scenarios, and station constraint scenarios. Then, corresponding to different constraint scenarios, the test data sets include satellite test data sets, constellation test data sets, and station test data sets. Figure 2 When constructing a plurality of experimental data sets corresponding to the preset constraint scenarios according to the ionospheric STEC data set, the following S201 to S203 may be included:

[0038] S201, obtaining observation data of each adjacent observation line of sight in the ionospheric STEC data set, and constructing a satellite test data set for the satellite constraint scenario based on the observation data of each adjacent observation line of sight.

[0039] It is understandable that in the satellite constraint scenario, the degree of influence of error sources such as signal quality and multipath effect in TEC observation is closely related to the elevation angle of observation. Specifically, the larger the elevation angle, the better the signal quality and the smaller the error caused by multipath effect. Therefore, it is crucial to consider the size of the elevation angle when dealing with these errors. In addition, the present disclosure also considers the line of sight angle of two adjacent observations. The line of sight angle is an important parameter that can characterize the proximity relationship between the puncture points of two adjacent observations. In the differential processing of TEC observations, the smaller the line of sight angle, the closer the puncture point, so it can be inferred that the VTEC difference will be smaller and the differential observation value will be more accurate. The satellite test data set mainly includes the observation data of each adjacent observation line of sight in the ionospheric STEC data set.

[0040] S202, obtaining observation data corresponding to different constellations in the ionospheric STEC data set, and constructing a constellation test data set for the constellation constraint scenario based on the observation data corresponding to the different constellations.

[0041] It is understandable that the accuracy of TEC observations may vary between different GNSS systems due to differences in their respective satellite orbit designs, signal structures, system coverage, and signal processing techniques. Specifically, different GNSS constellations have different satellite orbits and coverage, and these differences may result in different accuracy when conducting TEC observations in different regions. For example, GLONASS has a larger orbital inclination than the other three global systems, which may provide higher accuracy in high latitudes and relatively lower accuracy in low latitudes. In addition, the difference in signal structure and frequency of each constellation is also an important factor. Taking GPS and BDS as an example, when conducting dual-frequency observations of TEC, GPS's 1575.420 MHz and 1227.600 MHz and BDS's 1561.098 MHz and 1207.140 MHz are generally selected. This frequency difference between different constellations affects the signal's penetration ability and sensitivity to ionospheric delay.

[0042] Here, another important factor affecting the accuracy of TEC observations is the operating time of the constellation. Long-operating systems such as GPS and GLONASS are relatively superior to newly developed systems in terms of data volume and system stability due to their relatively mature technology and the accumulation of a large amount of observation data. The richness of these historical data is of great significance for improving the accuracy and reliability of TEC observations, because they can be used to calibrate new observation data and improve the accuracy of data processing. Constellation systems such as GPS, GLONASS, BDS, and GALILEO, although all aim to provide global positioning and navigation services, have significant differences in satellite orbit design, signal structure, coverage, and signal processing technology. These variations play an important role in determining the accuracy and reliability of total electron content (TEC) observations. For example, differences in satellite orbit altitude and inclination in these constellation systems may affect the angle and path of TEC observations, thereby affecting the values ​​obtained. In addition, the different signal frequencies and structures of each constellation system lead to different sensitivities to ionospheric delays, which in turn lead to different degrees of errors in TEC measurements. At the same time, the coverage of each constellation system may also have a significant impact on the observation accuracy in a specific region, resulting in differences in TEC observation accuracy in high or low latitudes, depending on the specific constellation system.

[0043] Therefore, it is crucial to consider the model errors caused by the constellation differences when fusing TEC observation data from different constellations. Therefore, the constellation test dataset includes the observation data corresponding to different constellations in the ionospheric STEC data set.

[0044] S203, obtaining observation data corresponding to different station spatial positions in the ionospheric STEC data set, and constructing a station test data set for the station constraint scenario based on the observation data corresponding to the different station spatial positions.

[0045] It is understandable that when analyzing the two TEC observations, special attention should be paid to the model errors caused by different stations in the local area. This error mainly comes from the differences in geographical location, instrument calibration, environmental conditions and other factors of different stations. The geographical location of each station determines the way of observing TEC. For example, stations located at different latitudes or altitudes may observe different ionospheric density changes, which will affect the measured value of TEC. In addition to the differences in geographical location, the types, performance and calibration status of receivers and antennas used by different stations may also be different. These equipment differences may lead to different processing of GNSS signals, thus affecting the measurement accuracy of TEC. In addition, the environmental conditions of each station, such as temperature, humidity, atmospheric pressure, etc., will also affect the performance of the equipment and the observation results. Therefore, when processing and analyzing TEC observation data from different stations, by analyzing the observation data corresponding to different stations, we can understand the performance and limitations of different stations, consider and weaken these model errors caused by station differences, and provide practical information for ionospheric research. The station test data set consists of observation data corresponding to the spatial positions of different stations in the ionospheric STEC data set.

[0046] Specifically, after determining the test data set corresponding to each preset constraint scenario, the scene constraint information corresponding to each test data set can be determined. To this end, it is necessary to construct an observation equation to obtain the scene constraint information corresponding to each constraint scenario. The observation equation is expressed as:

[0047]

[0048] ;

[0049] in, The VTEC value measured for the first time; The VTEC value measured the second time; Expressed as the projection function corresponding to the first measurement; Expressed as the projection function corresponding to the second measurement; It is expressed as the STEC value of the first measurement; It is expressed as the STEC value of the second measurement; The hardware delay of the station represented by the first measurement; represents the hardware delay of the station for the second measurement; The hardware delay of the satellite represented as the first measurement; Expressed as the hardware delay of the satellite for the second measurement.

[0050] It is understandable that, since the local changes of the ionosphere in a short period of time are relatively stable, an important assumption with physical significance is that if the two measurements are close in time and the puncture points are close in space, then the VTEC of the two measurements should be basically the same. Therefore, the left side of the above equation is The term is approximately 0. For this reason, in the precise modeling process of TEC, the present disclosure assumes that the VTEC difference between two measurements obeys a normal distribution with a specific variance. This variance not only quantifies the similarity between the two measurements, but also provides a statistical basis for evaluating the consistency of the measurement data. Therefore, after constructing the observation equation, the target constraint information (i.e., normal distribution constraint) can be determined based on the observation equation and the test data set corresponding to each preset constraint scenario. Figure 3 As shown, the following S301~S303 may be included:

[0051] S301, determining satellite constraint information based on the observation equation and the satellite test data set; determining constellation constraint information based on the observation equation and the constellation test data set, and determining station constraint information based on the observation equation and the station test data set.

[0052] Here, in the satellite constraint scenario, since the smaller the line of sight angle is, the closer the puncture point is, it can be inferred that the VTEC difference will be smaller and the differential observation value will be more accurate. Therefore, in the process of considering satellite constraint information, observations with smaller angles are usually given greater weight. Specifically, all TEC measurement pairs belonging to the same ionospheric grid in the satellite test data set can be searched. These measurement pairs can come from the same station, the same time, the same constellation but different satellites. Its satellite constraint information can be expressed as follows:

[0053] , ;

[0054] in, It is expressed as the minimum altitude angle between two observations in the satellite test data set; Expressed as the angle between two observations; Expressed as a correction factor for satellite constraints.

[0055] Here, when constructing constellation constraint information for the constellation constraint scenario, all TEC measurement pairs in the constellation experimental data set that belong to the same ionospheric grid and come from the same station and two different constellations at the same time can be searched, and the constellation constraint information can be expressed as:

[0056] , ;

[0057] in, It is expressed as the minimum altitude angle between two observations in the constellation test data set; Expressed as the correction factor of the constellation constraint; Expressed as the first constellation weight constant; Expressed as the second constellation weight constant.

[0058] For example, in the station constraint scenario, the spatial position difference of different stations is reflected in the distance between the puncture points of the two observations. This difference in spatial position has a significant impact on the differential observation value of TEC. The longer the IPP baseline length, the higher the spatial separation between the two stations, which may lead to greater observation value differences. In addition, the satellite observation altitude angle is also crucial for selecting high-quality observation data. Accordingly, the station constraint information is expressed as:

[0059] , ;

[0060] in, It is expressed as the minimum elevation angle between two observations in the station test data set; It is expressed as the distance between puncture points; Expressed as a correction factor for station constraints.

[0061] S302: construct time constraint information about the time constraint scenario based on the test data set, and adjust the satellite constraint information, the constellation constraint information and the station constraint information respectively based on the time constraint information.

[0062] For example, when constructing the observation equation, it is usually not required that the two measurements must be performed completely simultaneously. However, the time span between the two measurements has a significant impact on the difference in TEC observations, so the preset constraint scenario also includes a time constraint scenario. The larger the time span, the more obvious the difference between the two measurements will tend to be due to changes in the ionosphere state. In addition, the distribution of ionospheric electron density is also affected by the geomagnetic field. At different magnetic latitudes, the directional gradient of the puncture point will be different. For example, in low and high latitudes, the distribution of ionospheric electron density may be greatly affected by seasonal changes and geomagnetic activity, resulting in uneven distribution of electron density. Especially in the aurora region, ionospheric activity may be very intense, while in the equatorial region, strong equatorial ionospheric anomaly (EIA) effects are often observed. In contrast, in mid-latitudes, the distribution of ionospheric electron density is relatively uniform and less affected by seasonal changes. The ionospheric activity in this area is relatively mild, without the violent fluctuations in the aurora region, nor the significant EIA effect in the equatorial region. Therefore, TEC observations conducted in mid-latitude regions are often more stable and have relatively small errors. When constructing and analyzing TEC differential equations, the disclosed embodiments fully understand and consider these geomagnetic field and latitude characteristics to improve the accuracy and reliability of TEC observations. This in-depth understanding helps to perform more accurate TEC measurements in different regions and under different conditions.

[0063] Therefore, in order to impose time constraints on the two TEC observations, the time constraint information about the time constraint scenario can be constructed based on the experimental dataset and expressed as:

[0064] ;

[0065] in, It is expressed as the time interval between two observations; Expressed as geomagnetic latitude; They are respectively represented as the first correction coefficient and the second correction coefficient of the time constraint.

[0066] It is understandable that the time constraint scenario will affect the other three scenarios. Therefore, in order to express the constraint information more accurately, the satellite constraint information, constellation constraint information and station constraint information can be adjusted based on the time constraint information. The adjusted constraint information can be expressed as:

[0067] ,

[0068] ,

[0069] .

[0070] S303: Determine the target constraint information based on the adjusted satellite constraint information, the constellation constraint information, the station constraint information and the time constraint information.

[0071] After obtaining the adjusted satellite constraint information, constellation constraint information, station constraint information and time constraint information, the above constraint information is combined into a matrix to determine the target constraint information.

[0072] S103, calculating a DCB value based on a DCB calculation formula according to the target constraint information and the test data set, and obtaining VTEC data based on the DCB value, the test data set and a VTEC calculation formula.

[0073] Here, when calculating the DCB value, in order to improve the calculation efficiency and accuracy, each correction coefficient in the target constraint information can be optimized based on a preset optimization algorithm, wherein the optimization algorithm may include genetic algorithm, particle swarm optimization, simulated annealing, etc., which are not specifically limited here, and the purpose is to find the best correction coefficient that can make the calculation result closer to the actual or expected value. Then, according to the target constraint information and test data set corresponding to the optimized correction coefficient, the DCB value is calculated based on the DCB calculation formula.

[0074] In the embodiment of the present disclosure, a particle swarm optimization algorithm is used to optimize each correction coefficient, and the algorithm parameters are as follows:

[0075] ;

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] .

[0082] Here, the DCB calculation formula is expressed as:

[0083] ;

[0084] ;

[0085] in,

[0086] ;

[0087] ;

[0088] ;

[0089] in, Expressed as ; Represented as the target constraint information corresponding to the optimized correction coefficient.

[0090] It is understood that after the DCB value is calculated, the VTEC data can be calculated based on the DCB value, the test data set and the VTEC calculation formula to estimate the global ionospheric TEC. The VTEC calculation formula is expressed as:

[0091] ;

[0092] in, It is represented by the vertical electron content in the field of view of satellite j observed by station i in the test data set at epoch k; mf is represented by the projection function; It is expressed as the slant electron content in the field of view of satellite j observed by station i in the test data set at epoch k; It is represented as the hardware delay of station i; Denoted as the hardware delay of satellite j.

[0093] The global ionospheric TEC estimation method and device considering the spatiotemporal constraint relationship provided in the embodiments of the present disclosure fully consider the spatiotemporal relationship between the various preset constraint scenarios by constructing data sets corresponding to different preset constraint scenarios, and constructing constraint information corresponding to each scenario based on the data sets of different scenarios. Subsequently, the DCB parameters and ionospheric VTEC are estimated based on the constraint information and the experimental data set through the DCB calculation formula and the VTEC calculation formula. In this way, the consistency of the ionospheric TEC estimation results in time and space can be ensured.

[0094] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0095] Based on the same inventive concept, the embodiments of the present disclosure also provide a global ionospheric TEC estimation device that considers time and space constraints, which corresponds to the global ionospheric TEC estimation method that considers time and space constraints. Since the principle of solving the problem by the device in the embodiments of the present disclosure is similar to the global ionospheric TEC estimation method that considers time and space constraints in the embodiments of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0096] Reference Figure 4 FIG. 4 is a schematic diagram of a global ionospheric TEC estimation device 400 considering time and space constraints provided by an embodiment of the present disclosure, wherein the device comprises:

[0097] The data solving module 401 is used to obtain a GNSS observation file, construct a geometry-free combination of pseudorange and phase based on the GNSS observation file; and solve the geometry-free combination based on a phase smoothed pseudorange method to obtain an ionospheric STEC data set;

[0098] The constraint determination module 402 is used to construct a plurality of test data sets corresponding to preset constraint scenarios according to the ionospheric STEC data set; determine the scenario constraint information corresponding to each of the preset constraint scenarios based on the test data sets, and determine the target constraint information based on the scenario constraint information corresponding to the preset constraint scenarios;

[0099] The data calculation module 403 is used to calculate the DCB value based on the DCB calculation formula according to the target constraint information and the test data set, and to calculate the VTEC data based on the DCB value, the test data set and the VTEC calculation formula.

[0100] In some possible embodiments, the data solving module 401 is further used for:

[0101] An ephemeris file is obtained, and based on the ephemeris file, altitude angle data within a preset range in the solution result without geometric combination is eliminated to obtain the ionospheric STEC data set.

[0102] In some possible embodiments, the preset constraint scenario includes a satellite constraint scenario, a constellation constraint scenario, and a station constraint scenario; the test data set includes a satellite test data set, a constellation test data set, and a station test data set; the constraint determination module 402 is specifically used to:

[0103] Acquire observation data of each adjacent observation line of sight in the ionospheric STEC data set, and construct a satellite test data set for the satellite constraint scenario based on the observation data of each adjacent observation line of sight;

[0104] Acquire observation data corresponding to different constellations in the ionospheric STEC data set, and construct a constellation test data set for the constellation constraint scenario based on the observation data corresponding to the different constellations;

[0105] Observation data corresponding to different station spatial positions in the ionospheric STEC data set are obtained, and a station test data set for the station constraint scenario is constructed based on the observation data corresponding to the different station spatial positions.

[0106] In some possible embodiments, the constraint determination module 402 is further configured to:

[0107] Construct an observation equation; the observation equation includes:

[0108]

[0109] ;

[0110] in, The VTEC value measured for the first time; The VTEC value measured the second time; Expressed as the projection function corresponding to the first measurement; Expressed as the projection function corresponding to the second measurement; It is expressed as the STEC value of the first measurement; It is expressed as the STEC value of the second measurement; The hardware delay of the station represented by the first measurement; represents the hardware delay of the station for the second measurement; The hardware delay of the satellite represented as the first measurement; Expressed as the hardware delay of the satellite for the second measurement.

[0111] In some possible embodiments, the preset constraint scenario further includes a time constraint scenario, and the constraint determination module 402 is specifically configured to:

[0112] Determining satellite constraint information based on the observation equation and the satellite test data set; determining constellation constraint information based on the observation equation and the constellation test data set, and determining station constraint information based on the observation equation and the station test data set;

[0113] constructing time constraint information about the time constraint scenario based on the test data set, and adjusting the satellite constraint information, the constellation constraint information, and the station constraint information respectively based on the time constraint information;

[0114] Determine the target constraint information based on the adjusted satellite constraint information, the constellation constraint information, the station constraint information and the time constraint information;

[0115] The satellite constraint information includes:

[0116] , ;

[0117] in, It is expressed as the minimum altitude angle between two observations in the satellite test data set; Expressed as the angle between two observations; Expressed as the correction factor for satellite constraints;

[0118] The constellation constraint information includes:

[0119] , ;

[0120] in, It is expressed as the minimum altitude angle between two observations in the constellation test data set; Expressed as the correction factor of the constellation constraint; Expressed as the first constellation weight constant; Expressed as the second constellation weight constant;

[0121] The station constraint information includes:

[0122] , ;

[0123] in, It is expressed as the minimum elevation angle between two observations in the station test data set; It is expressed as the distance between puncture points; Expressed as the correction factor of the station constraint;

[0124] The time constraint information includes:

[0125] ;

[0126] in, It is expressed as the time interval between two observations; Expressed as geomagnetic latitude; They are respectively represented as the first correction coefficient and the second correction coefficient of the time constraint.

[0127] In some possible embodiments, the data calculation module 403 is specifically used to:

[0128] Optimizing each correction coefficient in the target constraint information based on a preset optimization algorithm, and calculating a DCB value based on the DCB calculation formula according to the target constraint information corresponding to the optimized correction coefficient and the test data set;

[0129] The DCB calculation formula includes:

[0130] ;

[0131] ;

[0132] in,

[0133] ;

[0134] ;

[0135] ;

[0136] in, Expressed as ; Represented as the target constraint information corresponding to the optimized correction coefficient.

[0137] In some possible embodiments, the VTEC calculation formula includes:

[0138] ;

[0139] in, It is represented by the vertical electron content in the field of view of satellite j observed by station i in the test data set at epoch k; mf is represented by the projection function; It is expressed as the slant electron content in the field of view of satellite j observed by station i in the test data set at epoch k; It is represented as the hardware delay of station i; Denoted as the hardware delay of satellite j.

[0140] Based on the same technical concept, the embodiment of the present disclosure also provides a computer device. Figure 5 , which is a schematic diagram of the structure of a computer device 500 provided in an embodiment of the present disclosure, including a processor 501, a memory 502, and a bus 503. The memory 502 is used to store execution instructions, including a memory 5021 and an external memory 5022; the memory 5021 is also called an internal memory, which is used to temporarily store the operation data in the processor 501 and the data exchanged with the external memory 5022 such as a hard disk. The processor 501 exchanges data with the external memory 5022 through the memory 5021.

[0141] In the embodiment of the present application, the memory 502 is specifically used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 501. That is, when the computer device 500 is running, the processor 501 communicates with the memory 502 through the bus 503, so that the processor 501 executes the application code stored in the memory 502, and then executes the method described in any of the above embodiments.

[0142] Among them, the memory 502 can be, but is not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

[0143] Processor 501 may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0144] It is to be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the computer device 500. In other embodiments of the present application, the computer device 500 may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0145] The embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the global ionospheric TEC estimation method considering the spatiotemporal constraint relationship described in the above method embodiment are executed. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0146] The embodiments of the present disclosure also provide a computer program product, which carries a program code. The instructions included in the program code can be used to execute the steps of the global ionospheric TEC estimation method considering the spatiotemporal constraints described in the above method embodiment. For details, please refer to the above method embodiment, which will not be repeated here.

[0147] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0148] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed system and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0149] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0150] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0151] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0152] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A global ionospheric TEC estimation method considering spatiotemporal constraints, characterized in that: include: Obtain GNSS observation files, and construct a geometry-free combination of pseudorange and phase based on the GNSS observation files; and solving the geometry-free combination based on a phase-smoothed pseudorange method to obtain an ionospheric STEC data set; Acquire observation data of each adjacent observation line of sight in the ionospheric STEC data set, and construct a satellite test data set for a satellite constraint scenario based on the observation data of each adjacent observation line of sight; acquire observation data corresponding to different constellations in the ionospheric STEC data set, and construct a constellation test data set for a constellation constraint scenario based on the observation data corresponding to the different constellations; Acquire observation data corresponding to different station spatial positions in the ionospheric STEC data set, and construct a station test data set for a station constraint scenario based on the observation data corresponding to the different station spatial positions; Determining satellite constraint information based on the observation equation and the satellite test data set; determining constellation constraint information based on the observation equation and the constellation test data set, and determining station constraint information based on the observation equation and the station test data set; Constructing time constraint information about a time constraint scenario based on a test data set, and adjusting the satellite constraint information, the constellation constraint information, and the station constraint information based on the time constraint information; wherein the test data set includes a satellite test data set, a constellation test data set, and a station test data set; The satellite constraint information includes: ， ； in, It is expressed as the minimum altitude angle between two observations in the satellite test data set; Expressed as the angle between two observations; Expressed as the correction factor for satellite constraints; The constellation constraint information includes: ， ； in, It is expressed as the minimum altitude angle between two observations in the constellation test data set; Expressed as the correction factor of the constellation constraint; Expressed as the first constellation weight constant; Expressed as the second constellation weight constant; The station constraint information includes: ， ； in, It is expressed as the minimum elevation angle between two observations in the station test data set; It is expressed as the distance between puncture points; Expressed as the correction factor of the station constraint; The time constraint information includes: ； in, It is expressed as the time interval between two observations; Expressed as geomagnetic latitude; They are respectively represented as the first correction coefficient and the second correction coefficient of the time constraint; The target constraint information is determined based on the adjusted satellite constraint information, the constellation constraint information, the station constraint information and the time constraint information; and a DCB value is calculated based on a DCB calculation formula according to the target constraint information and the test data set, and VTEC data is calculated based on the DCB value, the test data set and the VTEC calculation formula.

2. The method according to claim 1, characterized in that: After solving the geometry-free combination based on the phase smoothing pseudorange method, the method includes: An ephemeris file is obtained, and based on the ephemeris file, altitude angle data within a preset range in the solution result without geometric combination is eliminated to obtain the ionospheric STEC data set.

3. The method according to claim 1, characterized in that The observation equation includes: ； in, The VTEC value measured for the first time; The VTEC value measured the second time; Expressed as the projection function corresponding to the first measurement; Expressed as the projection function corresponding to the second measurement; It is expressed as the STEC value of the first measurement; It is expressed as the STEC value of the second measurement; The hardware delay of the station represented by the first measurement; represents the hardware delay of the station for the second measurement; The hardware delay of the satellite represented as the first measurement; Expressed as the hardware delay of the satellite for the second measurement.

4. The method according to claim 3, characterized in that The calculating the DCB value based on the DCB calculation formula according to the target constraint information and the test data set includes: Optimizing each correction coefficient in the target constraint information based on a preset optimization algorithm, and calculating a DCB value based on the DCB calculation formula according to the target constraint information corresponding to the optimized correction coefficient and the test data set; The DCB calculation formula includes: ； ； in, ； ； ； in, Expressed as ; Represented as the target constraint information corresponding to the optimized correction coefficient.

5. The method according to claim 1, characterized in that: The VTEC calculation formula includes: ； in, It is represented by the vertical electron content in the field of view of satellite j observed by station i in the test data set at epoch k; mf is represented by the projection function; It is expressed as the slant electron content in the field of view of satellite j observed by station i in the test data set at epoch k; It is represented as the hardware delay of station i; Denoted as the hardware delay of satellite j.

6. A global ionospheric TEC estimation device considering time and space constraints, characterized in that: include: A data solution module is used to obtain GNSS observation files and construct a geometry-free combination of pseudorange and phase based on the GNSS observation files; and solving the geometry-free combination based on a phase-smoothed pseudorange method to obtain an ionospheric STEC data set; A data set construction module is used to obtain observation data of each adjacent observation line of sight in the ionospheric STEC data set, and construct a satellite test data set for a satellite constraint scenario based on the observation data of each adjacent observation line of sight; obtain observation data corresponding to different constellations in the ionospheric STEC data set, and construct a constellation test data set for a constellation constraint scenario based on the observation data corresponding to the different constellations; Acquire observation data corresponding to different station spatial positions in the ionospheric STEC data set, and construct a station test data set for a station constraint scenario based on the observation data corresponding to the different station spatial positions; a constraint determination module, configured to determine satellite constraint information based on an observation equation and the satellite test data set; determine constellation constraint information based on the observation equation and the constellation test data set, and determine station constraint information based on the observation equation and the station test data set; Constructing time constraint information about a time constraint scenario based on a test data set, and adjusting the satellite constraint information, the constellation constraint information, and the station constraint information based on the time constraint information; wherein the test data set includes a satellite test data set, a constellation test data set, and a station test data set; The satellite constraint information includes: ， ； in, It is expressed as the minimum altitude angle between two observations in the satellite test data set; Expressed as the angle between two observations; Expressed as the correction factor for satellite constraints; The constellation constraint information includes: ， ； in, It is expressed as the minimum altitude angle between two observations in the constellation test data set; Expressed as the correction factor of the constellation constraint; Expressed as the first constellation weight constant; Expressed as the second constellation weight constant; The station constraint information includes: ， ； in, It is expressed as the minimum elevation angle between two observations in the station test data set; It is expressed as the distance between puncture points; Expressed as the correction factor of the station constraint; The time constraint information includes: ； in, It is expressed as the time interval between two observations; Expressed as geomagnetic latitude; They are respectively represented as the first correction coefficient and the second correction coefficient of the time constraint; A data calculation module is used to determine target constraint information based on the adjusted satellite constraint information, the constellation constraint information, the station constraint information and the time constraint information; and calculate a DCB value based on a DCB calculation formula according to the target constraint information and the test data set, and calculate VTEC data based on the DCB value, the test data set and the VTEC calculation formula.

7. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

8. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

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