A method and device for detecting ionospheric disturbance based on GNSS dual-frequency data

Abstract

The application relates to a GNSS double-frequency data-based ionospheric disturbance detection method and device, belonging to the field of space science and technology, wherein the GNSS double-frequency data-based ionospheric disturbance detection method comprises the following steps: acquiring a TEC sequence of the ionosphere, detecting ionospheric disturbance at a target moment based on the observation difference values of the two moments before the target moment in the TEC sequence, obtaining an ionospheric disturbance sequence corresponding to the TEC sequence in time sequence; acquiring an initial three-dimensional ionospheric model, correcting the initial three-dimensional ionospheric model based on the ionospheric disturbance sequence, obtaining a three-dimensional ionospheric disturbance model, and detecting ionospheric disturbance based on the three-dimensional ionospheric disturbance model. The application effectively improves the accuracy of ionospheric disturbance detection.

Description

Technical Field

[0001] This invention relates to the field of space science and technology, and in particular to a method and apparatus for detecting ionospheric disturbances based on GNSS dual-frequency data. Background Technology

[0002] In research fields such as space physics and seismology, accurate monitoring and analysis of ionospheric disturbances are crucial. Ionospheric disturbance reconstruction is a process of three-dimensional or two-dimensional inversion of ionospheric electron density and its disturbance characteristics through various observation techniques and algorithms.

[0003] Most existing ionospheric perturbation reconstruction schemes reconstruct ionospheric perturbations by mathematically inverting the total electron content (TEC). However, existing technologies inevitably have errors in obtaining TEC, and mathematical inversion will amplify this error, resulting in insufficient accuracy of the final ionospheric perturbation reconstruction results.

[0004] Therefore, how to accurately reconstruct ionospheric disturbances has become an urgent technical problem to be solved. Summary of the Invention

[0005] In view of this, it is necessary to provide a method and device for ionospheric disturbance detection based on GNSS dual-frequency data to solve the problem of insufficient accuracy in current ionospheric disturbance detection.

[0006] To address the aforementioned problems, in a first aspect, the present invention provides an ionospheric disturbance detection method based on GNSS dual-frequency data, comprising:

[0007] The TEC sequence of the ionosphere is obtained, and the ionospheric disturbance at the target time is detected based on the observation difference between the two time points before the target time in the TEC sequence, so as to obtain the ionospheric disturbance sequence that corresponds to the TEC sequence in time.

[0008] An initial three-dimensional model of the ionosphere is obtained. The initial three-dimensional model of the ionosphere is then corrected based on the ionospheric perturbation sequence to obtain a three-dimensional model of the ionospheric perturbation. Ionospheric perturbation is then detected based on the three-dimensional model of the ionospheric perturbation.

[0009] In one possible implementation, detecting ionospheric disturbances at the target time based on the observed difference between the two time points preceding the target time in the TEC sequence includes:

[0010] The estimated rate of change of TEC between the two moments preceding the target time is determined based on the observational differences between the two moments preceding the target time.

[0011] Ionospheric disturbances at the target time are detected based on the observational differences between the target time and the time preceding the target time, and the estimated rate of change of TEC between the two time points preceding the target time.

[0012] In one possible implementation, the estimated rate of change of TEC between the two time points preceding the target time is determined based on the following formula:

[0013]

[0014]

[0015] in, This represents the rate of change of TEC between the two time points preceding the target time. , Indicates the first TEC observations at time 10:00 and Indicates different observation frequencies, Represents frequency Next Time and the Differences in carrier observations at different times, Represents frequency Next The carrier wave observation value at time.

[0016] In one possible implementation, the ionospheric perturbation at the target time is determined based on the following formula:

[0017]

[0018] in, This represents the ionospheric disturbance at the target time. This represents the rate of change of TEC between the two time points preceding the target time. Indicates the first TEC observations at time 10:00 and Indicates different observation frequencies, Represents frequency Next Time and the Differences in carrier observations at different times.

[0019] In one possible implementation, the initial three-dimensional ionospheric model is obtained by integrating the ionospheric electron density along the observation ray, wherein the integration of the ionospheric electron density along the observation ray includes:

[0020] Based on the TEC background value and TEC increase rate, the ionospheric electron density is integrated along the observation ray. The TEC background value is used to represent the average TEC value under preset geomagnetic conditions and preset solar radiation within a preset time period. The TEC increase rate is used to represent the degree of change of TEC relative to the TEC background value.

[0021] In one possible implementation, obtaining the initial three-dimensional model of the ionosphere includes:

[0022] The ionosphere is divided into four layers: 0-150km, 150-450km, 450-750km, and 750-1050km. The model parameters of each layer of the initial ionospheric perturbation three-dimensional model are estimated based on the Kalman filter algorithm.

[0023] In one possible implementation, the estimation of model parameters for each layer of the initial ionospheric perturbation 3D model based on the Kalman filter algorithm includes:

[0024] A fitting function for electron density is constructed based on latitude and solar hour angle;

[0025] The coefficients of the polynomial to be estimated in the fitting function of the electron density are determined based on the Kalman filter algorithm.

[0026] In one possible implementation, determining the estimated polynomial coefficients in the fitting function of the electron density based on the Kalman filter algorithm includes:

[0027] A state equation is established to predict the coefficients of the polynomial to be estimated, and an observation equation is established based on the mapping relationship between electron density and TEC observation difference. The coefficients of the polynomial to be estimated in the fitting function of the electron density are determined based on the Kalman filter algorithm.

[0028] In one possible implementation, the step of correcting the initial ionospheric three-dimensional model based on the ionospheric perturbation sequence to obtain an ionospheric perturbation three-dimensional model includes:

[0029] Based on the sampling time corresponding to the ionospheric perturbation sequence, the ionospheric perturbation sequence is transformed from the time dimension to the spatial dimension;

[0030] Based on the ionospheric perturbation sequence after dimensional transformation, the initial ionospheric three-dimensional model is corrected according to the height to obtain the ionospheric perturbation three-dimensional model.

[0031] On the other hand, the present invention also provides an ionospheric disturbance detection device based on GNSS dual-frequency data, comprising:

[0032] The detection module is used to acquire the TEC sequence of the ionosphere, and detect the ionospheric disturbance at the target time based on the observation difference between the two time points before the target time in the TEC sequence, so as to obtain the ionospheric disturbance sequence that corresponds to the TEC sequence in time.

[0033] A construction module is used to obtain an initial ionospheric 3D model, correct the initial ionospheric 3D model based on the ionospheric perturbation sequence to obtain an ionospheric perturbation 3D model, and perform ionospheric perturbation detection based on the ionospheric perturbation 3D model.

[0034] Secondly, the present invention also provides a detection device, including a memory and a processor, wherein,

[0035] The memory is used to store programs;

[0036] The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the ionospheric disturbance detection method based on GNSS dual-frequency data described in any of the above implementations.

[0037] Thirdly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the ionospheric disturbance detection method based on GNSS dual-frequency data described in any of the above implementations.

[0038] The beneficial effects of this invention are as follows: The ionospheric disturbance detection method and apparatus based on GNSS dual-frequency data provided by this invention first detects ionospheric disturbances through historical observation differences, improving the accuracy of ionospheric disturbance detection. Then, it corrects the initial ionospheric three-dimensional model through the ionospheric disturbances to obtain a three-dimensional model of ionospheric disturbances, thus completing the construction of the three-dimensional model of ionospheric disturbances. This provides a more intuitive representation of ionospheric disturbances and further improves the accuracy of ionospheric disturbance detection. This invention effectively improves the accuracy of ionospheric disturbance detection. Attached Figure Description

[0039] Figure 1 A schematic flowchart of an embodiment of the ionospheric disturbance detection method based on GNSS dual-frequency data provided by the present invention;

[0040] Figure 2 A schematic flowchart of an embodiment of the ionospheric disturbance detection process based on GNSS dual-frequency data provided by the present invention;

[0041] Figure 3 A schematic diagram of an embodiment of the ionospheric disturbance detection device based on GNSS dual-frequency data provided by the present invention;

[0042] Figure 4 This is a schematic diagram of an embodiment of the detection device provided by the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0044] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0045] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0046] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0047] Ionospheric disturbances: Ionospheric disturbances refer to abnormal changes in the electron density, ion composition, or dynamic properties of the Earth's ionosphere, usually caused by solar activity, geomagnetic activity, or human activities. These disturbances can affect radio communications, satellite navigation, and spacecraft operations.

[0048] TEC: TEC is a parameter describing the total number of free electrons per unit area in a vertical column of the ionosphere, usually expressed in TECU (Total Electron Content Unit), where 1 TECU = 10¹ 6 Electrons per square meter. It is an important indicator for studying ionospheric properties, satellite communications, and the impact of space weather.

[0049] This invention provides a method and apparatus for detecting ionospheric disturbances based on GNSS dual-frequency data, which will be described below.

[0050] Figure 1 This is a schematic flowchart of an embodiment of the ionospheric disturbance detection method based on GNSS dual-frequency data provided by the present invention, as shown below. Figure 1 As shown, the ionospheric disturbance detection method based on GNSS dual-frequency data includes:

[0051] S101. Obtain the TEC sequence of the ionosphere, and detect the ionospheric disturbance at the target time based on the observation difference between the two time points before the target time in the TEC sequence, so as to obtain the ionospheric disturbance sequence that corresponds to the TEC sequence in time.

[0052] It should be noted that the TEC sequence of the ionosphere can be determined using dual-frequency observation data from the Global Navigation Satellite System (GNSS) combined with the carrier phase combination method, thus ensuring the accuracy of TEC measurements and providing a foundation for subsequent ionospheric disturbance determination. Furthermore, by constructing a second-order operator based on the TEC observation differences between the two time points preceding the target time, ionospheric disturbances at the target time can be detected. This approach effectively detects ionospheric disturbances based on historical observation differences, thereby improving the accuracy of ionospheric disturbance detection.

[0053] S102. Obtain an initial three-dimensional model of the ionosphere, correct the initial three-dimensional model of the ionosphere based on the ionospheric perturbation sequence to obtain a three-dimensional model of the ionospheric perturbation, and perform ionospheric perturbation detection based on the three-dimensional model of the ionospheric perturbation.

[0054] It should be noted that the initial three-dimensional model of the ionosphere can be obtained by integrating the electron density of the ionosphere along the observation ray. However, by correcting the initial three-dimensional model of the ionosphere through the ionospheric perturbation sequence, the ionospheric perturbation can be displayed more intuitively, thereby further improving the accuracy of ionospheric perturbation detection.

[0055] In summary, the ionospheric disturbance detection method based on GNSS dual-frequency data provided in this invention first detects ionospheric disturbances by identifying differences in historical observations, thus improving the accuracy of ionospheric disturbance detection. Then, it corrects the initial ionospheric 3D model using the ionospheric disturbance data to obtain a 3D ionospheric disturbance model, completing the construction of the 3D ionospheric disturbance model. This provides a more intuitive representation of ionospheric disturbances and further improves the accuracy of ionospheric disturbance detection. This invention effectively improves the accuracy of ionospheric disturbance detection.

[0056] Combination Figure 2 Here are the specific steps for ionospheric disturbance detection based on GNSS dual-frequency data:

[0057] 1. Estimation of key parameters of the ionosphere.

[0058] Using dual-frequency GNSS observation data, the key ionospheric parameter TEC is accurately estimated based on specific observation equations and calculation methods. The TEC calculation process is further optimized by subtracting from the observation equations and considering multipath effects. The method for estimating the key ionospheric parameter includes the following steps:

[0059] First, the key ionospheric parameter TEC is estimated using dual-frequency GNSS observation data. The basic equations for the GNSS observations are as follows:

[0060]

[0061]

[0062]

[0063]

[0064] in, , , and These are carrier phase observations and pseudorange observations at two different frequencies; and These are carrier wave observations measured in meters; and They are and The wavelength; It is the geometric distance between the receiver and the satellite, including frequency-independent errors. For ionospheric delay, The ratio parameter represents the relationship between the ionospheric delays of two carrier observations. , . and This is the ambiguity parameter. and For the code deviation between the satellite and the receiver, and This refers to the carrier phase deviation between the satellite and the receiver. and These represent the satellite and the receiver, respectively. It represents two different frequencies. and These are multipath error and measurement noise, respectively.

[0065] Secondly, the difference between the observation equations for the two frequencies yields the following formula:

[0066]

[0067]

[0068] in, It is the difference in noise levels between two frequencies. and These are differential code biases (DCBs), where , , , .

[0069] Then, to reduce the impact of multipath effects on TEC estimation, a suitable height cutoff angle (15°) is selected. For dual-frequency GPS observations, the ionospheric delay at the L1 frequency can be expressed as:

[0070]

[0071] Furthermore, TEC can be represented as:

[0072]

[0073]

[0074] 2. Ionospheric disturbance detection.

[0075] The second-order operator is used to process the TEC sequence to detect ionospheric disturbances, and the time and location of the disturbances are determined by combining the coordinates of the ionospheric puncture point.

[0076] In some embodiments of the present invention, the detection of ionospheric disturbances at the target time based on the observed difference between the two time points preceding the target time in the TEC sequence includes:

[0077] The estimated rate of change of TEC between the two moments preceding the target time is determined based on the observational differences between the two moments preceding the target time.

[0078] Ionospheric disturbances at the target time are detected based on the observational differences between the target time and the time preceding the target time, and the estimated rate of change of TEC between the two time points preceding the target time.

[0079] It should be noted that: by using a second-order operator to process the TEC sequence, the current normal ionospheric change is predicted by subtracting the observation difference between the previous two periods, and then removing the ionospheric change in the current period, any abnormal changes can be detected.

[0080] Suppose we have a set of TEC time series:

[0081]

[0082] The ionospheric perturbation in the i-th epoch can be determined by the TEST value given by the following formula:

[0083]

[0084] In some embodiments of the present invention, the estimated rate of change of TEC between the two time points preceding the target time can be determined based on the following formula:

[0085]

[0086]

[0087] in, This represents the rate of change of TEC between the two time points preceding the target time. , Indicates the first TEC observations at time 10:00 and Indicates different observation frequencies, Represents frequency Next Time and the Differences in carrier observations at different times, Represents frequency Next The carrier wave observation value at time.

[0088] In some embodiments of the present invention, the ionospheric disturbance at the target time can be determined based on the following formula:

[0089]

[0090] in, This represents the ionospheric disturbance at the target time. This represents the rate of change of TEC between the two time points preceding the target time. Indicates the first TEC observations at time 10:00 and Indicates different observation frequencies, Represents frequency Next Time and the Differences in carrier observations at different times.

[0091] 3. Construction and optimization of three-dimensional model of ionospheric disturbance.

[0092] By reasonably selecting background values ​​and calculating the TEC increase rate, a foundation is provided for subsequent analysis. The three-dimensional model of ionospheric perturbation is constructed and optimized by integrating along the observation ray and using the Kalman filter method.

[0093] In some embodiments of the present invention, the initial three-dimensional ionospheric model is obtained by integrating the ionospheric electron density along the observation ray, wherein integrating the ionospheric electron density along the observation ray includes:

[0094] Based on the TEC background value and TEC increase rate, the ionospheric electron density is integrated along the observation ray. The TEC background value is used to represent the average TEC value under preset geomagnetic conditions and preset solar radiation within a preset time period. The TEC increase rate is used to represent the degree of change of TEC relative to the TEC background value.

[0095] It should be noted that a background value needs to be selected first in order to extract fluctuation information. Based on geomagnetic conditions (Dst, Kp index) and solar radiation (F10.7 index) data, the average TEC value during periods of relatively calm geomagnetic and solar radiation is used as the background value. The formula for calculating the average TEC value is:

[0096]

[0097] In addition, the TEC increase rate can be used to measure the change in the total electron content of the ionosphere relative to a selected background value. The formula for calculating the TEC increase rate is:

[0098]

[0099] In some embodiments of the present invention, obtaining the initial three-dimensional model of the ionosphere includes:

[0100] The ionosphere is divided into four layers: 0-150km, 150-450km, 450-750km, and 750-1050km. The model parameters of each layer of the initial ionospheric perturbation three-dimensional model are estimated based on the Kalman filter algorithm.

[0101] It should be noted that the ionospheric electron density integration process is divided into four layers (150km, 450km, 750km, and 1050km) along the observation ray. Then, a mapping function is used to apply the single-layer model to each integration part. By using the Kalman filter method to estimate the model parameters layer by layer, an initial three-dimensional model of ionospheric perturbation can be obtained.

[0102] In some embodiments of the present invention, the estimation of model parameters for each layer of the initial ionospheric perturbation three-dimensional model based on the Kalman filter algorithm includes:

[0103] A fitting function for electron density is constructed based on latitude and solar hour angle;

[0104] The coefficients of the polynomial to be estimated in the fitting function of the electron density are determined based on the Kalman filter algorithm.

[0105] It should be noted that the following function can be used to fit the electron density:

[0106]

[0107] in, Let be the coefficients of the polynomial to be estimated, b be the latitude, and s be the solar hour angle. Then, the Kalman filter method is used to estimate the model parameters layer by layer, finally obtaining a three-dimensional model of ionospheric perturbation.

[0108] In some embodiments of the present invention, determining the estimated polynomial coefficients in the fitting function of the electron density based on the Kalman filter algorithm includes:

[0109] A state equation is established to predict the coefficients of the polynomial to be estimated, and an observation equation is established based on the mapping relationship between electron density and TEC observation difference. The coefficients of the polynomial to be estimated in the fitting function of the electron density are determined based on the Kalman filter algorithm.

[0110] It should be noted that the steps for estimating model parameters using Kalman filtering are as follows:

[0111] The state equation is used to predict the polynomial coefficients of the electron density in each layer (state vector). (corresponding to the electron density of the four height layers), the expression is:

[0112]

[0113] in, Here is the state transition matrix. This is the process noise vector.

[0114] The observation equation establishes a mapping relationship between electron density and the difference in TEC observations, expressed as:

[0115]

[0116] in For the observed difference value of TEC, For the observation matrix, This is the observed noise vector.

[0117] The electron density estimates for each layer are corrected using the differences in TEC observations through an iterative process of Kalman filtering (prediction-update):

[0118]

[0119]

[0120]

[0121] in, This represents the state prediction at time k based on the estimate at time k-1. The covariance matrix represents the predicted state; Represents the Kalman gain matrix; This represents the state update value after fusing the observation data; The covariance matrix representing the updated state; This is a weighting matrix, which modifies the weights of the observations by transforming the measurement noise matrix. , The elevation angle is the angle observed by the satellite. The lower the elevation angle, the greater the multipath noise. The larger the value, the greater the measurement noise, and the lower the weight of the observed value.

[0122] In some embodiments of the present invention, the step of correcting the initial three-dimensional ionospheric model based on the ionospheric perturbation sequence to obtain a three-dimensional ionospheric perturbation model includes:

[0123] Based on the sampling time corresponding to the ionospheric perturbation sequence, the ionospheric perturbation sequence is transformed from the time dimension to the spatial dimension;

[0124] Based on the ionospheric perturbation sequence after dimensional transformation, the initial ionospheric three-dimensional model is corrected according to the height to obtain the ionospheric perturbation three-dimensional model.

[0125] It should be noted that when correcting the initial 3D ionospheric model based on the ionospheric disturbance sequence, the ionospheric disturbance sequence can be transformed from a time dimension to a spatial dimension according to the sampling time corresponding to the ionospheric disturbance sequence, thereby determining the ionospheric disturbance at different altitudes. Then, based on the ionospheric disturbance at different altitudes, the initial 3D ionospheric model can be corrected according to altitude to obtain a 3D ionospheric disturbance model, thus visually reflecting the ionospheric disturbance from a height perspective and ensuring the accuracy of ionospheric disturbance reconstruction.

[0126] This invention achieves efficient and accurate reconstruction of ionospheric disturbances by precisely estimating key ionospheric parameters, effectively detecting disturbances, and constructing an optimized 3D model. In key parameter estimation, GNSS dual-frequency observation data is used, and a series of precise observation equations and corresponding processing, such as difference operations, consideration of multipath effects, and selection of appropriate cutoff angles, are employed to accurately calculate the TEC (Temperature Effort Capacity). For disturbance detection, a second-order operator is used to process the TEC sequence, combined with the coordinates of ionospheric penetration points, to accurately detect the time and location of disturbances. In the optimization of the 3D model, a reasonable background value is selected. The average TEC value is determined based on geomagnetic and solar radiation data as the background, the TEC increase rate is calculated, and layered integration along the observation rays is performed using Kalman filtering to ensure the model's accuracy and reliability. This invention overcomes the problems of inaccurate key parameter estimation, low disturbance detection efficiency, and poor 3D model performance in traditional ionospheric disturbance research. It can flexibly handle ionospheric disturbance analysis under different conditions, exhibiting strong adaptability and providing higher-quality data support for fields such as space physics and seismology, thus powerfully promoting research progress in related fields.

[0127] To better implement the ionospheric disturbance detection method based on GNSS dual-frequency data in this embodiment of the invention, based on the ionospheric disturbance detection method based on GNSS dual-frequency data, correspondingly, as follows: Figure 3 As shown, this embodiment of the invention also provides an ionospheric disturbance detection device based on GNSS dual-frequency data. The ionospheric disturbance detection device 300 based on GNSS dual-frequency data includes:

[0128] The detection module 301 is used to acquire the TEC sequence of the ionosphere, detect the ionospheric disturbance at the target time based on the observation difference value between the two time points before the target time in the TEC sequence, and obtain the ionospheric disturbance sequence that corresponds to the TEC sequence in time.

[0129] The construction module 302 is used to obtain an initial three-dimensional model of the ionosphere, correct the initial three-dimensional model of the ionosphere based on the ionosphere perturbation sequence to obtain a three-dimensional model of the ionosphere perturbation, and perform ionosphere perturbation detection based on the three-dimensional model of the ionosphere perturbation.

[0130] The ionospheric disturbance detection device 300 based on GNSS dual-frequency data provided in the above embodiments can realize the technical solutions described in the embodiments of the ionospheric disturbance detection method based on GNSS dual-frequency data. The specific implementation principles of each module or unit can be found in the corresponding content in the embodiments of the ionospheric disturbance detection method based on GNSS dual-frequency data, and will not be repeated here.

[0131] like Figure 4 As shown, the present invention also provides a detection device 400. The detection device 400 includes a processor 401, a memory 402, and a display 403. Figure 4 Only some components of the detection device 400 are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0132] In some embodiments, processor 401 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 402 or process data, such as the magnetic resonance image optimization method of the present invention.

[0133] In some embodiments, processor 401 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 401 may be local or remote. In some embodiments, processor 401 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-cloud, etc., or any combination thereof.

[0134] In some embodiments, memory 402 may be an internal storage unit of the detection device 400, such as a hard disk or memory of the detection device 400. In other embodiments, memory 402 may also be an external storage device of the detection device 400, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the detection device 400.

[0135] Furthermore, the memory 402 may include both internal storage units of the detection device 400 and external storage devices. The memory 402 is used to store the application software and various types of data installed on the detection device 400.

[0136] In some embodiments, display 403 may be an LED display, a liquid crystal display, a touch-screen liquid crystal display, or an organic light-emitting diode (OLED) touchscreen, etc. Display 403 is used to display information from the detection device 400 and to display a visual user interface. Components 401-403 of the detection device 400 communicate with each other via a system bus.

[0137] In one embodiment, when the processor 401 executes the ionospheric disturbance detection program based on GNSS dual-frequency data in the memory 402, the following steps can be implemented:

[0138] The TEC sequence of the ionosphere is obtained, and the ionospheric disturbance at the target time is detected based on the observation difference between the two time points before the target time in the TEC sequence, so as to obtain the ionospheric disturbance sequence that corresponds to the TEC sequence in time.

[0139] An initial three-dimensional model of the ionosphere is obtained. The initial three-dimensional model of the ionosphere is then corrected based on the ionospheric perturbation sequence to obtain a three-dimensional model of the ionospheric perturbation. Ionospheric perturbation is then detected based on the three-dimensional model of the ionospheric perturbation.

[0140] It should be understood that when the processor 401 executes the ionospheric disturbance detection program based on GNSS dual-frequency data in the memory 402, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.

[0141] Furthermore, this embodiment of the invention does not specifically limit the type of the detection device 400 mentioned. The detection device 400 can be a portable electronic device such as a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, or laptop computer. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic devices can also be other portable electronic devices, such as laptop computers with touch-sensitive surfaces (e.g., touch panels). It should also be understood that in some other embodiments of the invention, the detection device 400 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0142] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions of the ionospheric disturbance detection method based on GNSS dual-frequency data provided in the above-described method embodiments.

[0143] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0144] The above provides a detailed description of the ionospheric disturbance detection method and apparatus based on GNSS dual-frequency data provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for detecting ionospheric disturbances based on GNSS dual-frequency data, characterized in that, include: The TEC sequence of the ionosphere is obtained, and the ionospheric disturbance at the target time is detected based on the observation difference between the two time points before the target time in the TEC sequence, so as to obtain the ionospheric disturbance sequence that corresponds to the TEC sequence in time. An initial three-dimensional model of the ionosphere is obtained, and the initial three-dimensional model of the ionosphere is corrected based on the ionospheric perturbation sequence to obtain an ionospheric perturbation three-dimensional model. Ionospheric perturbation is then detected based on the ionospheric perturbation three-dimensional model. The detection of ionospheric disturbances at the target time based on the observed difference between the two time points preceding the target time in the TEC sequence includes: The estimated rate of change of TEC between the two moments preceding the target time is determined based on the observational differences between the two moments preceding the target time. Based on the observational differences between the target time and the time before the target time, and the estimated TEC change rate between the two times before the target time, the ionospheric disturbance at the target time is detected. The estimated rate of change of TEC between the two time points preceding the target time is determined based on the following formula: in, This represents the rate of change of TEC between the two time points preceding the target time. Indicates the first TEC observations at time 10:00 Indicates the first TEC observations at time 10:00 and Indicates different observation frequencies, Represents frequency Next Time and the Difference in carrier observations at different times, Represents frequency Next The carrier wave observation at time; The ionospheric disturbance at the target time is determined based on the following formula: in, This represents the ionospheric disturbance at the target time. This represents the rate of change of TEC between the two time points preceding the target time. Indicates the first TEC observations at time 10:00 and Indicates different observation frequencies, Represents frequency Next Time and the Differences in carrier observations at different times.

2. The ionospheric disturbance detection method based on GNSS dual-frequency data according to claim 1, characterized in that, The initial three-dimensional ionospheric model is obtained by integrating the ionospheric electron density along the observation ray. This integration of the ionospheric electron density along the observation ray includes: Based on the TEC background value and TEC increase rate, the ionospheric electron density is integrated along the observation ray. The TEC background value is used to represent the average TEC value under preset geomagnetic conditions and preset solar radiation within a preset time period. The TEC increase rate is used to represent the degree of change of TEC relative to the TEC background value.

3. The ionospheric disturbance detection method based on GNSS dual-frequency data according to claim 2, characterized in that, The acquisition of the initial three-dimensional model of the ionosphere includes: The ionosphere is divided into four layers: 0-150km, 150-450km, 450-750km, and 750-1050km. The model parameters of each layer of the initial three-dimensional ionosphere model are estimated based on the Kalman filter algorithm.

4. The ionospheric disturbance detection method based on GNSS dual-frequency data according to claim 3, characterized in that, The estimation of model parameters for each layer of the initial ionospheric 3D model based on the Kalman filter algorithm includes: A fitting function for electron density is constructed based on latitude and solar hour angle; The coefficients of the polynomial to be estimated in the fitting function of the electron density are determined based on the Kalman filter algorithm.

5. The ionospheric disturbance detection method based on GNSS dual-frequency data according to claim 4, characterized in that, The determination of the estimated polynomial coefficients in the fitting function for the electron density based on the Kalman filter algorithm includes: A state equation is established to predict the coefficients of the polynomial to be estimated, and an observation equation is established based on the mapping relationship between electron density and TEC observation difference. The coefficients of the polynomial to be estimated in the fitting function of the electron density are determined based on the Kalman filter algorithm.

6. The ionospheric disturbance detection method based on GNSS dual-frequency data according to claim 1, characterized in that, The step of correcting the initial ionospheric three-dimensional model based on the ionospheric perturbation sequence to obtain an ionospheric perturbation three-dimensional model includes: Based on the sampling time corresponding to the ionospheric perturbation sequence, the ionospheric perturbation sequence is transformed from the time dimension to the spatial dimension; Based on the ionospheric perturbation sequence after dimensional transformation, the initial ionospheric three-dimensional model is corrected according to the height to obtain the ionospheric perturbation three-dimensional model.

7. An ionospheric disturbance detection device based on GNSS dual-frequency data, characterized in that, include: The detection module is used to acquire the TEC sequence of the ionosphere, and detect the ionospheric disturbance at the target time based on the observation difference between the two time points before the target time in the TEC sequence, so as to obtain the ionospheric disturbance sequence that corresponds to the TEC sequence in time. A construction module is used to obtain an initial three-dimensional model of the ionosphere, correct the initial three-dimensional model of the ionosphere based on the ionosphere perturbation sequence to obtain a three-dimensional model of the ionosphere perturbation, and perform ionosphere perturbation detection based on the three-dimensional model of the ionosphere perturbation. The detection of ionospheric disturbances at the target time based on the observed difference between the two time points preceding the target time in the TEC sequence includes: The estimated rate of change of TEC between the two moments preceding the target time is determined based on the observational differences between the two moments preceding the target time. Based on the observational differences between the target time and the time before the target time, and the estimated TEC change rate between the two times before the target time, the ionospheric disturbance at the target time is detected. The estimated rate of change of TEC between the two time points preceding the target time is determined based on the following formula: in, This represents the rate of change of TEC between the two time points preceding the target time. Indicates the first TEC observations at time 10:00 Indicates the first TEC observations at time 10:00 and Indicates different observation frequencies, Represents frequency Next Time and the Difference in carrier observations at different times, Represents frequency Next The carrier wave observation at time; The ionospheric disturbance at the target time is determined based on the following formula: in, This represents the ionospheric disturbance at the target time. This represents the rate of change of TEC between the two time points preceding the target time. Indicates the first TEC observations at time 10:00 and Indicates different observation frequencies, Represents frequency Next Time and the Differences in carrier observations at different times.

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