A GNSS-based equatorial plasma bubble detection method and system

Abstract

The present invention discloses a method and system for detecting equatorial plasma bubbles based on GNSS, which relates to the technical field of equatorial plasma bubble detection. The method includes the following steps: obtaining a GNSS observation file received by a receiver from a satellite and extracting pseudorange data from the GNSS observation file; extracting TEC data from the GNSS observation file according to the extracted pseudorange data; extracting an equatorial plasma bubble sequence based on the extracted TEC data; detecting equatorial plasma bubbles according to the equatorial plasma bubble sequence to determine an equatorial plasma bubble event; wherein the TEC data is the total electron content calculated using the GNSS observation file. Through the method provided by the present invention, the problems of cycle slip detection and TEC information extraction with ambiguity resolution when the perturbation of TEC data extraction using carrier phase data is large can be effectively solved, and at the same time, the workload of equatorial plasma bubble detection can be effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of equatorial plasma bubble detection, and particularly to a method and system for equatorial plasma bubble detection based on GNSS. Background Art

[0002] The ionosphere is an ionized region of the Earth's atmosphere, mainly located in the atmosphere more than 60 kilometers above the Earth's surface, and is a plasma composed of free electrons, positive and negative ions, neutral molecules and atoms. TEC (Total Electron Content), that is, the total electron content, is an important parameter describing the characteristics of the ionosphere. It refers to the total number of free electrons that a radio wave passes through when passing through the ionosphere at a unit frequency. 1 TEC unit is equal to 10 16 electrons per square meter.

[0003] Equatorial Plasma Bubble (EPB for short), the formation of the equatorial plasma bubble is considered to be caused by the Rayleigh-Taylor (R-T) instability in the F layer. After sunset, the electric field in the equatorial region will increase, which may cause the F layer to rise to a higher altitude, thereby increasing the growth rate of the R-T instability. This phenomenon mainly occurs in the F layer of the equatorial region, usually after sunset, from night to midnight. EPBs can have a serious impact on navigation and communication systems because they interfere with radio signals propagating through the ionosphere, causing signal attenuation or scintillation.

[0004] Cycle slip is a special phenomenon in the carrier phase observation of the Global Navigation Satellite System (GNSS). It refers to the jump of the integer number of cycles in the counter when the receiver re-acquires the signal due to the temporary interruption or loss of lock of the satellite signal. This phenomenon will have a significant impact on the positioning accuracy. Therefore, the detection and repair of cycle slips are important links in GNSS data processing.

[0005] Therefore, in order to ensure the continuity and reliability of the carrier phase observation values; improve the positioning accuracy and the reliability of the positioning results; cycle slip detection is particularly necessary.

[0006] Commonly used cycle slip detection methods include:

[0007] 1. Dual-frequency code-phase combination method (Melbourne-Wubbena combination, referred to as M-W combination): Using the dual-frequency carrier phase and code-phase pseudo-range combined observations to detect / repair cycle slips. The M-W combination eliminates the influence of the ionosphere, troposphere, clock error and geometric distance. When there is no cycle slip, the M-W test quantity should fluctuate around a constant. When a cycle slip occurs, the test quantity will change suddenly.

[0008] 2. Dual - frequency Ionospheric Residual Method (without geometric distance, GF combination method): Using the ionospheric residuals of dual - frequency carrier phase observations to detect / repair cycle slips. The GF combination eliminates the influence of geometric distance, is applicable to both static and dynamic observation data, and is significantly affected by the sampling interval of observations and the satellite elevation angle.

[0009] Before processing carrier phase data, generally these two methods are combined to monitor and eliminate cycle slips, which involves a large amount of work and will result in a certain loss of data.

[0010] In summary, in the existing equatorial plasma bubble detection technology, when processing TEC using carrier phase data, the cycle slip detection link is required first. Inevitably, some data will be deleted. Especially when the TEC perturbation is large, it is easy to misidentify some non - cycle - slip data as cycle slips due to the cycle slip detection link, resulting in the loss of some available data. Moreover, there is an ambiguity resolution problem in extracting TEC information using carrier observations, leading to a large workload in equatorial plasma bubble detection. Summary of the Invention

[0011] In order to overcome the problems of cycle slip detection and ambiguity resolution in extracting TEC information using carrier observations in the prior art, which lead to data loss and a large workload in equatorial plasma bubble detection, the present invention proposes a GNSS - based equatorial plasma bubble detection method and system, which can effectively solve the problems of cycle slip detection when the TEC perturbation is large and ambiguity resolution in extracting TEC information, and at the same time effectively reduce the workload of equatorial plasma bubble detection.

[0012] The object of the present invention is achieved by the following technical solutions:

[0013] A GNSS - based equatorial plasma bubble detection method, the method includes the following steps:

[0014] Receive the GNSS observation file transmitted by the satellite, and extract pseudorange data from the data in the GNSS observation file;

[0015] Extract TEC data from the GNSS observation file according to the extracted pseudorange data;

[0016] Extract the equatorial plasma bubble sequence based on the extracted TEC data;

[0017] Detect equatorial plasma bubbles according to the equatorial plasma bubble sequence to determine equatorial plasma bubble events;

[0018] Wherein, the TEC data is the total electron content calculated using the GNSS observation file.

[0019] In the above technical solution, by extracting the pseudorange data from the GNSS observation file, the problem of cycle slip detection when the TEC perturbation is large can be effectively solved. By extracting the TEC data from the GNSS observation file according to the extracted pseudorange data, the problems of cycle slip detection and ambiguity resolution in the process of extracting TEC information can be effectively solved. Furthermore, based on the extracted TEC data, the equatorial plasma bubble sequence is extracted, and equatorial plasma bubble detection is carried out to determine the equatorial plasma bubble event. When cycle slips are frequent or the perturbation is large, the observation of the equatorial plasma bubble can be completed, and the workload of equatorial plasma bubble detection is effectively reduced.

[0020] Furthermore, the process of extracting pseudorange data from the data in the GNSS observation file includes:

[0021] Extract the dual-frequency pseudorange observations in the GNSS observation file, and the expression is:

[0022] ;

[0023] According to the extracted dual-frequency pseudorange observations, ionospheric analysis is carried out to obtain the difference between the code pseudorange observations, and the expression is:

[0024] ;

[0025] Wherein, represents the distance between the satellite and the station; both represent dual-frequency observations; represents the pseudorange measurement; represents the speed of light; respectively represent the clock differences between satellite i and receiver j; is the tropospheric delay; represents the receiver error and satellite hardware error at different frequencies; represents the ionospheric delay at different frequencies; represents the signal frequency, and with the superscript 2 both representing square, and the subscripts 1, 2 representing frequencies 1, 2.

[0026] In the above technical solution, by using the dual-frequency pseudorange observations in the GNSS observation file and carrying out ionospheric analysis on the difference between the extracted dual-frequency pseudorange observations, some errors in the data processing process can be effectively eliminated.

[0027] Furthermore, the process of extracting TEC data from the GNSS observation file according to the extracted pseudorange data includes:

[0028] Based on the code pseudorange observations, extract the ionospheric TEC data, and the expression is:

[0029] ;

[0030] Among them, is the signal frequency; and are respectively the code pseudorange observation values at the frequencies; B is the hardware delay deviation between frequencies; represents using and to obtain the ionospheric TEC.

[0031] Furthermore, the extracted ionospheric TEC data is absolutized to extract the absolute TEC data, and the expression is:

[0032] ;

[0033] Among them, and with the superscript 2 both representing square, and the subscripts 1 and 2 representing frequency 1 and frequency 2.

[0034] In the above technical solution, extracting the TEC data in the GNSS observation file according to the extracted pseudorange data can effectively solve the problems of cycle slip detection and ambiguity resolution in the process of extracting TEC information because the pseudorange data is relatively easy to obtain and has no cycle slips.

[0035] Furthermore, preprocess the absolute TEC data by removing gross errors and obtain the broadcast ephemeris file;

[0036] Fuse the broadcast ephemeris file with the preprocessed absolute TEC data to obtain a new TEC data file;

[0037] Use SG filtering to smooth and denoise the new TEC data file, and perform fitting processing on the smoothed and denoised TEC data file, and use the obtained TEC data file after fitting as the background TEC data file;

[0038] Among them, the SG filtering represents Savitzky - Golay filtering.

[0039] Furthermore, the process of using SG filtering to smooth, denoise and fit the new TEC data file includes:

[0040] Define a window size of ;

[0041] Within the window, use order polynomial to fit the data points;

[0042] Calculate the smoothed value of the center point of the window, and the expression is:

[0043] ;

[0044] wherein, represents the window half-width, represents the total number of points in the window, , represents the coefficient of the polynomial.

[0045] In the above technical solution, by performing gross error rejection on the extracted absolute TEC data, the data quality and the TEC estimation accuracy can be effectively improved. For the judgment of gross errors, the stability of the pseudorange observation values can be improved; by using SG filtering to perform smoothing, noise reduction, and fitting processing on the TEC data file, the shape and characteristics of the signal can be maintained, providing higher stability and reliability for the subsequent extraction of the equatorial plasma bubble sequence.

[0046] Furthermore, the process of extracting the equatorial plasma bubble sequence based on the extracted TEC data includes:

[0047] By using the smoothed and noise-reduced TEC data file and the background TEC data file, calculate the detrended TEC sequence in the TEC data file, and the expression is:

[0048] ;

[0049] Determine the start time and end time of the equatorial plasma bubble sequence according to the detrended TEC sequence, and perform linear fitting on the start time and end time to obtain a partially fitted TEC sequence;

[0050] Subtract the TEC sequence in the smoothed and noise-reduced TEC data file from the TEC sequence in the partially fitted TEC data file to obtain the equatorial plasma bubble sequence;

[0051] wherein, represents the satellite elevation angle, represents the original TEC sequence after smoothing and noise reduction, FittedTEC represents the fitted TEC data, and the TEC sequence represents the time series of the TEC data in the TEC data file.

[0052] Furthermore, the process of determining the equatorial plasma bubble event includes:

[0053] Set up a linear regression model to learn the data change law of the equatorial plasma bubble sequence, and obtain an equatorial plasma bubble detection model that masters the data change law of the equatorial plasma bubble sequence;

[0054] Use the equatorial plasma bubble detection model to judge the equatorial plasma bubble event according to the equatorial plasma bubble sequence, and the judgment expression is:

[0055] ;

[0056] Among them, the event represents an equatorial plasma bubble event, MaxdTEC represents the maximum TEC depletion in the extracted equatorial plasma bubble sequence, and Duration represents the duration in the equatorial plasma bubble sequence.

[0057] Furthermore, the process of determining the start time and end time of the equatorial plasma bubble sequence based on the detrended TEC sequence and performing linear fitting on the start time and end time includes:

[0058] Define a straight line , for any two points and on the straight line, calculate the slope of the straight line y, and the expression is:

[0059] ;

[0060] According to the slope m, calculate the intercept of the straight line y, and the expression is:

[0061] ;

[0062] Based on the slope m and the intercept b, obtain the linear fitting equation, and the expression is:

[0063] ;

[0064] Among them, y represents a straight line in the TEC data, and x represents the time data.

[0065] In the above technical solution, the detrended TEC sequence (dTEC) can be obtained by subtracting the original TEC before fitting from the TEC after fitting, which helps to obtain the start time and end time of the EPBs sequence, and using the dTEC sequence (modifieddTEC) to extract the equatorial plasma bubble sequence can better obtain a good TEC depletion curve and can more intuitively determine the equatorial plasma bubble event from the data.

[0066] A GNSS-based equatorial plasma bubble detection system, the system includes:

[0067] A receiving module for receiving GNSS observation files transmitted by satellites;

[0068] A processing module for extracting pseudorange data from the data in the GNSS observation file and extracting TEC data in the GNSS observation file according to the extracted pseudorange data;

[0069] The processing module is further used to extract the equatorial plasma bubble sequence based on the extracted TEC data;

[0070] A detection module, configured to detect equatorial plasma bubbles according to the equatorial plasma bubble sequence and determine equatorial plasma bubble events;

[0071] Wherein, the TEC data is the total electron content calculated using GNSS observation files.

[0072] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0073] The present invention provides a method and system for detecting equatorial plasma bubbles based on GNSS. By extracting pseudorange data from GNSS observation files, it can effectively solve the problem of cycle slip detection when TEC perturbations are large. By extracting TEC data from the GNSS observation files based on the extracted pseudorange data, it can effectively solve the problems of cycle slip detection and ambiguity resolution in the process of extracting TEC information. Furthermore, based on the extracted TEC data, an equatorial plasma bubble sequence is extracted, and equatorial plasma bubble detection is performed to determine equatorial plasma bubble events. It can complete the observation of equatorial plasma bubbles when cycle slips are frequent or perturbations are large, and effectively reduce the workload of equatorial plasma bubble detection. Description of the Drawings

[0074] Figure 1 It is a flowchart of the steps of a method for detecting equatorial plasma bubbles based on GNSS provided by an embodiment of the present application;

[0075] Figure 2 It is a schematic diagram of the principle of detecting equatorial plasma bubbles based on GNSS provided by an embodiment of the present application;

[0076] Figure 3 It is a comparison chart of satellite data experiments in 2014 provided by an embodiment of the present application;

[0077] Figure 4 It is a comparison chart of satellite data experiments in 2023 provided by an embodiment of the present application;

[0078] Figure 5 It is a structural block diagram of a system for detecting equatorial plasma bubbles based on GNSS provided by an embodiment of the present application. Detailed Embodiments

[0079] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Preferred embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0080] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs. The terms used in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0081] Embodiment 1:

[0082] This embodiment provides a method for detecting equatorial plasma bubbles based on GNSS. Refer to Figure 1 , the method includes the following steps:

[0083] Step S1: Receive the GNSS observation file transmitted by the satellite, and extract the pseudorange data from the data in the GNSS observation file;

[0084] Step S2: Extract the TEC data in the GNSS observation file according to the extracted pseudorange data;

[0085] Step S3: Extract the equatorial plasma bubble sequence based on the extracted TEC data;

[0086] Step S4: Detect the equatorial plasma bubbles according to the equatorial plasma bubble sequence, and determine the equatorial plasma bubble event;

[0087] Among them, the TEC data is the total electron content calculated using the GNSS observation file.

[0088] As a preferred embodiment, in step S1, refer to Figure 2 , the process of extracting the pseudorange data from the data in the GNSS observation file includes:

[0089] Extract the dual-frequency pseudorange observations in the GNSS observation file, and the expression is:

[0090] (1)

[0091] Among them, represents the distance between the satellite and the station; both represent the dual-frequency observations; represents the pseudorange observation; represents the speed of light; respectively represent the clock differences between satellite i and receiver j; is the tropospheric delay; represents the receiver error and satellite hardware error at different frequencies; represents the ionospheric delay at different frequencies.

[0092] Among them, in the case of only considering the first-order ionospheric delay error, the expression is:

[0093] (2)

[0094] Among them, TEC represents the total electron content, with the unit of 10 16 electrons ; f is the signal frequency, with the unit of The unit of is m. Combining equations (1) and (2), the equation can be written in the following form:

[0095] (3)

[0096] Among them, ; k = 1, 2.

[0097] Based on the extracted dual-frequency pseudorange observations, ionospheric analysis is performed to obtain the difference between the code pseudorange observations, and the expression is:

[0098] (4)

[0099] Among them, are the hardware delays of receiver j and satellite i, respectively.

[0100] It can be understood that by using the dual-frequency pseudorange observations in the GNSS observation file and the difference between the extracted dual-frequency pseudorange observations for ionospheric analysis, some errors in the data processing process can be effectively eliminated.

[0101] In step S2, the process of extracting TEC data from the GNSS observation file based on the extracted pseudorange data includes:

[0102] Based on the code pseudorange observations, ionospheric TEC data is extracted, and the expression is:

[0103] (5)

[0104] Among them, is the signal frequency; , are respectively the code pseudorange observations at frequencies; B is the hardware delay deviation between frequencies; represents using and frequencies to obtain the ionospheric TEC, and The superscript 2 of both represents square, and the subscripts 1 and 2 represent frequencies 1 and 2.

[0105] Furthermore, referring to Figure 2 , the extracted ionospheric TEC data is processed for absoluteness to extract absolute TEC data, and the expression is:

[0106] (6)

[0107] It can be understood that extracting the TEC data in the GNSS observation file according to the extracted pseudorange data can effectively solve the problems of cycle slip detection and ambiguity resolution in the process of extracting TEC information, not only because the pseudorange data is relatively easy to obtain and has no cycle slips.

[0108] As a preferred embodiment, refer to Figure 2 , preprocess the absolute TEC data and obtain the broadcast ephemeris file;

[0109] Fuse the broadcast ephemeris file with the preprocessed absolute TEC data to obtain a new TEC data file;

[0110] Use SG filtering to smooth and denoise the new TEC data file, and perform fitting processing on the smoothed and denoised TEC data file. Take the obtained TEC data file after fitting as the background TEC data file;

[0111] Specifically, calculating the coordinates and satellite altitude angle MF through the broadcast ephemeris file, the process of preprocessing the absolute TEC data includes removing the absolute TEC data with a satellite altitude cut-off angle lower than 15°, retaining the absolute TEC data with an altitude cut-off angle greater than or equal to 15°, and then further screening these data to remove the data with a continuous time less than 20 minutes, completing the removal of gross errors from the extracted absolute TEC data;

[0112] Among them, the SG filtering represents Savitzky-Golay filtering.

[0113] Specifically, the process of using SG filtering to smooth, denoise and fit the new TEC data file includes:

[0114] Define a window size of ;

[0115] Within the window, use order polynomial to fit the data points;

[0116] Calculate the smoothed value of the center point of the window, and the expression is:

[0117] (7)

[0118] Among them, represents the half-width of the window, represents the total number of points in the window, , represents the coefficient of the polynomial.

[0119] Specifically, a data sequence is set , and for each data point smoothing is performed. A window containing data points is selected, where is half of the window width.

[0120] For each window, a th-degree polynomial is selected to fit the data points within the window. The form of the polynomial is as follows:

[0121]

[0122] where are the coefficients of the polynomial, which are determined by the least squares method to make the polynomial best fit the data points within the window.

[0123] For each data point in the window, there is:

[0124]

[0125] where ranges from to , is the error term. The goal is to minimize the sum of the squares of the errors:

[0126]

[0127] By solving this least squares problem, the coefficients can be obtained. According to the coefficients of the polynomial, the estimated value of the center point of the window can be calculated, that is:

[0128]

[0129] This process is repeated for the entire data sequence to obtain the smoothed data sequence .

[0130] It can be understood that by removing gross errors from the extracted absolute TEC data, the data quality and the TEC estimation accuracy can be effectively improved. For the judgment of gross errors, the stability of the pseudorange observations can be improved; by using SG filtering to perform smoothing, noise reduction and fitting processing on the TEC data file, the shape and characteristics of the signal can be maintained, providing higher stability and reliability for the subsequent extraction of the equatorial plasma bubble sequence.

[0131] In step S3, the process of extracting the equatorial plasma bubble sequence based on the extracted TEC data includes:

[0132] From the smoothed and noise-reduced TEC data file and the background TEC data file, calculate the TEC sequence of the detrended term in the TEC data file. The expression is:

[0133] (8)

[0134] Determine the start time and end time of the equatorial plasma bubble sequence based on the TEC sequence of the detrended term, and perform linear fitting on the start time and end time to obtain a partially fitted TEC sequence;

[0135] Subtract the TEC sequence in the smoothed and noise-reduced TEC data file from the TEC sequence in the partially fitted TEC data file to obtain the equatorial plasma bubble sequence;

[0136] Among them, MF represents the satellite elevation angle, and the MF is calculated from the broadcast ephemeris file. represents the original TEC sequence after smoothed and noise-reduced processing, FittedTEC represents the fitted TEC data, and the TEC sequence represents the time series of TEC data in the TEC data file.

[0137] Furthermore, the process of determining the equatorial plasma bubble event includes:

[0138] Set up a linear regression model to learn the data change law of the equatorial plasma bubble sequence, and obtain an equatorial plasma bubble detection model that masters the data change law of the equatorial plasma bubble sequence;

[0139] Use the equatorial plasma bubble detection model to judge the equatorial plasma bubble event according to the equatorial plasma bubble sequence. The judgment expression is:

[0140] (9)

[0141] Among them, (equatorial plasma bubble event) represents the equatorial plasma bubble event, MaxdTEC represents the maximum TEC depletion in the extracted equatorial plasma bubble sequence, and Duration represents the duration in the equatorial plasma bubble sequence.

[0142] The TEC depletion phenomenon mainly occurs in spring and autumn seasons and is generally concentrated at night. If the maximum TEC depletion (i.e., depth) is greater than 5 TECU and the duration is greater than 10 minutes, the TEC depletion can be determined as an EPBs event.

[0143] Furthermore, the process of determining the start time and end time of the equatorial plasma bubble sequence based on the TEC sequence of the detrended term and performing linear fitting on the start time and end time includes:

[0144] Define a straight line , for any two points on the straight line and , calculate the slope of the straight line y, and the expression is:

[0145] ;

[0146] According to the slope m, use one of the points to solve for the y-axis intercept , and the expression is:

[0147] ;

[0148] Substitute the value of into the above formula to get:

[0149] ;

[0150] After combining, the expression for calculating the y-intercept of the straight line can be obtained as:

[0151] ;

[0152] Based on the slope m and the intercept b, the linear fitting equation is obtained, and the expression is:

[0153] ;

[0154] Among them, y represents a straight line in the TEC data, and x represents the time data.

[0155] It can be understood that by subtracting the original TEC before fitting from the TEC after fitting, the detrended TEC sequence (dTEC) can be obtained, which helps to obtain the start time and end time of the EPBs sequence. And using the dTEC sequence (modifieddTEC) to extract the equatorial plasma bubble sequence can better obtain a good TEC depletion curve, and can more intuitively determine the equatorial plasma bubble event from the data.

[0156] In this embodiment, by extracting the pseudorange data in the GNSS observation file, the problem of cycle slip detection when the TEC perturbation is large can be effectively solved. According to the extracted pseudorange data, the TEC data in the GNSS observation file is extracted, and the problems of cycle slip detection and ambiguity resolution in the process of extracting TEC information can be effectively solved. Furthermore, based on the extracted TEC data, the equatorial plasma bubble sequence is extracted, and equatorial plasma bubble detection is carried out to determine the equatorial plasma bubble event. When cycle slips are frequent or the perturbation is large, the observation of the equatorial plasma bubble can be completed, and the workload of equatorial plasma bubble detection is effectively reduced.

[0157] Embodiment 2:

[0158] This embodiment provides corresponding experimental data for Embodiment 1, which is as follows:

[0159] Taking the location of the HKSL station in Hong Kong (22.372°N, 113.928°E) as an example:

[0160] The two selected times are 2014 and 2023, both in the high solar activity years, and equatorial plasma bubble (EPB) events are more likely to occur.

[0161] ① On February 14, 2014 (day of year 45), the HKSL station detected the equatorial plasma bubble phenomenon through the G01 satellite from 15:45UT to 16:15UT.

[0162] A: Input the observation file and ephemeris file into the equatorial plasma bubble detection model. The model will use the pseudorange data extracted from the GNSS observation file (o file, sampling rate of 30s) of the HKSL station obtained at the IGS station. According to formula (6), the total electron content (TEC) data obtained by dual-frequency pseudorange measurement can be obtained;

[0163] B: No preprocessing such as cycle slip detection is required. Only the judgment of satellite elevation angle and data with less continuous time needs to be carried out. The data with too low satellite elevation angle (<15°) and the data with continuous time less than 20 minutes are screened out;

[0164] C: The obtained TEC data is denoised using Savitzky-Golay filtering (SG filtering). The set order is 3, and the sliding window is 330s (11 data), so that the obtained TEC data is smoother and prepares for subsequent work;

[0165] D: The denoised TEC data is fitted using SG filtering. The set order is 3, and the sliding window is 1 hour (121 data), and the background TEC can be obtained. Then subtract the fitted data (background TEC) from the original data, and the detrended TEC data (dTEC) can be obtained, which is generally used to judge the change of TEC data.

[0166] E: According to the dTEC data, the start time and end time of the data change can be obtained, that is, the start time and end time of the EPB time. From this, the data during this period can be linearly fitted to obtain the partially fitted TEC data.

[0167] After subtracting the TEC data and the partially fitted TEC data, the improved dTEC data can be obtained. Compared with the dTEC calculated at the beginning, this data can better reflect information such as the size of the EPB depletion and is conducive to the analysis of EPB events.

[0168] See Figure 3 , Figure 3 a TEC data (ObservedTEC), fitted TEC data (FittedTEC), and partially fitted TEC data (PartialFittedTEC) obtained using carrier phase data; Figure 3 b dTEC data (dTEC) and modified dTEC data (ModifeddTEC) obtained using carrier phase data; Figure 3 c TEC data (ObservedTEC), fitted TEC data (FittedTEC), and partially fitted TEC data (PartialFittedTEC) obtained using pseudorange data; Figure 3 d dTEC data (dTEC) and modified dTEC data (ModifeddTEC) obtained using pseudorange data.

[0169] It can be seen from Figure 3 that the EPB depletion obtained by using carrier phase data is 12 TECU, and the duration of EPB occurrence is 30 min; while the EPB depletion obtained by using pseudorange data is 10 TECU, and the EPB occurrence time is 30 min. It can be judged that an EPB event occurred during this time period using both types of data, and the obtained EPB depletion is basically the same. Therefore, the EPB data extracted by the method proposed in the present invention is feasible and reliable.

[0170] ② On February 18, 2023 (day of year 49), the HKSL station detected the equatorial plasma bubble phenomenon through satellite G17 from 11:57 UT to 13:15 UT.

[0171] TEC data can be obtained according to the same steps as in ①. By comparing the TEC data obtained using carrier phase data and pseudorange data, it can be seen that due to cycle slip detection, the carrier phase data filters out the data during the occurrence of the EPB event.

[0172] See Figure 4 , Figure 4 a TEC data obtained using carrier phase data; Figure 4 b TEC data obtained using pseudorange data; Figure 4 c TEC data (ObservedTEC), fitted TEC data (FittedTEC), and partially fitted TEC data (PartialFittedTEC) obtained using pseudorange data; Figure 4 d dTEC data (dTEC) and modified dTEC data (ModifieddTEC) obtained using pseudorange data.

[0173] In this processing, the obtained TEC data is denoised using Savitzky-Golay filtering (SG filtering). Since the noise of the pseudorange data obtained for this satellite is relatively large, the order is set to 3 and the sliding window is 15 minutes (31 data points), making the obtained TEC data smoother;

[0174] The denoised TEC data is subjected to fitting processing. Using SG filtering, the order is also set to 3 and the sliding window is 1 hour (121 data points), and the background TEC can be obtained. Then, subtracting the fitted data (background TEC) from the original data, the detrended TEC data (dTEC) can be obtained, which is generally used to judge the change of TEC data.

[0175] As can be seen from Figure 4 a, after a series of operations such as calculating the integer ambiguity and cycle slip detection on the carrier phase data, the data during the EPB event is deleted, resulting in the inability to extract relevant information about EPB; while Figure 4 it can be seen from b that the pseudorange data during the EPB event is reliable, without excessive perturbations and jumps, and can be utilized. After processing according to the previous steps, the EPB depletion of this event obtained using the pseudorange data is approximately 33 TECU, and the perturbation time is 78 minutes; thus, it can be judged that an EPB event occurred during this time period.

[0176] Embodiment 3:

[0177] This embodiment provides a GNSS-based equatorial plasma bubble detection system. Referring to Figure 5 , the system includes:

[0178] A receiving module for receiving GNSS observation files transmitted by satellites;

[0179] A processing module for extracting pseudorange data from the data in the GNSS observation file and extracting TEC data in the GNSS observation file according to the extracted pseudorange data;

[0180] The processing module is further configured to extract an equatorial plasma bubble sequence based on the extracted TEC data;

[0181] A detection module for detecting equatorial plasma bubbles according to the equatorial plasma bubble sequence to determine equatorial plasma bubble events;

[0182] Wherein, the TEC data is the total electron content calculated using the GNSS observation file.

[0183] The above are only embodiments of the present invention, and do not thereby limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall similarly be included within the patent protection scope of the present invention.

Claims

1. A method for detecting equatorial plasma bubbles based on GNSS, characterized in that: The method comprises the following steps: Receive GNSS observation files transmitted by satellites, and extract pseudorange data from the data in the GNSS observation files; Extract TEC data in the GNSS observation file based on the extracted pseudorange data; Extract the equatorial plasma bubble sequence based on the extracted TEC data; performing equatorial plasma bubble detection according to the equatorial plasma bubble sequence to determine the equatorial plasma bubble event; Among them, TEC data is the total electron content calculated using GNSS observation files.

2. The GNSS-based equatorial plasma bubble detection method according to claim 1, characterized in that: The process of extracting pseudorange data from the data in the GNSS observation file includes: Extract the dual-frequency pseudorange observation value from the GNSS observation file. The expression is: ； According to the extracted dual-frequency pseudorange observations, the ionospheric analysis is performed to obtain the difference between the code measurement pseudorange observations, which is expressed as: ； in, Indicates the distance between the satellite and the station; All represent bi-frequency observations; represents the pseudorange observation; represents the speed of light; represent the clock difference between satellite i and receiver j respectively; is the tropospheric delay; Represents receiver errors and satellite hardware errors at different frequencies; Represents the ionospheric delay at different frequencies; represents the signal frequency, and The superscript 2 represents square, and the subscripts 1 and 2 represent frequency 1 and frequency 2.

3. The GNSS-based equatorial plasma bubble detection method according to claim 2, characterized in that: The process of extracting TEC data in the GNSS observation file based on the extracted pseudorange data includes: Based on the code measurement pseudorange observation value, the ionospheric TEC data is extracted, and the expression is: ； in, is the signal frequency; , They are The code measurement pseudorange observation value on the frequency; B is the inter-frequency hardware delay deviation; Indicates the use and The ionospheric TEC is obtained by frequency.

4. The GNSS-based equatorial plasma bubble detection method according to claim 3, characterized in that: The extracted ionospheric TEC data is processed absolutely to extract the absolute TEC data. The expression is: ； in, and The superscript 2 represents square, and the subscripts 1 and 2 represent frequency 1 and frequency 2.

5. The GNSS-based equatorial plasma bubble detection method according to claim 4, characterized in that: Perform preprocessing to remove gross errors on the absolute TEC data and obtain the broadcast ephemeris file; The broadcast ephemeris file is merged with the pre-processed absolute TEC data to obtain a new TEC data file; The SG filter is used to smooth and reduce noise on the new TEC data file, and the smoothed and reduced noise TEC data file is fitted, and the TEC data file obtained after fitting is used as the background TEC data file; The SG filter represents Savitzky-Golay filter.

6. The GNSS-based equatorial plasma bubble detection method according to claim 5, characterized in that: The process of using SG filtering to smooth, reduce noise and fit the new TEC data file includes: Define a window size of ; In the window, use Order polynomial fit to data points; Calculate the window center point The smoothing value of , the expression is: ； in, Indicates the half-width of the window, Represents the total number of points in the window, , Represents the coefficients of the polynomial.

7. The GNSS-based equatorial plasma bubble detection method according to claim 5, characterized in that: The process of extracting the equatorial plasma bubble sequence based on the extracted TEC data includes: By smoothing the denoised TEC data file and the background TEC data file, the TEC sequence of the detrended item in the TEC data file is calculated. The expression is: ； The start and end time of the equatorial plasma bubble sequence is determined according to the detrended TEC sequence, and the start and end time are linearly fitted to obtain a partially fitted TEC sequence; The TEC sequence in the smoothed and denoised TEC data file is subtracted from the TEC sequence in the partially fitted TEC data file to obtain the equatorial plasma bubble sequence; in, represents the satellite altitude angle, represents the original TEC sequence after smoothing and noise reduction, FittedTEC represents the TEC data after fitting, and TEC sequence represents the time series of TEC data in the TEC data file.

8. The GNSS-based equatorial plasma bubble detection method according to claim 7, characterized in that: The process of identifying equatorial plasma bubble events involves: A linear regression model is set up to learn the data variation law of the equatorial plasma bubble sequence, and an equatorial plasma bubble detection model that grasps the data variation law of the equatorial plasma bubble sequence is obtained; The equatorial plasma bubble detection model is used to judge the equatorial plasma bubble event according to the equatorial plasma bubble sequence. The judgment expression is: ； in, Event represents the equatorial plasma bubble event, MaxdTEC represents the maximum TEC depletion in the extracted equatorial plasma bubble sequence, and Duration represents the duration in the equatorial plasma bubble sequence.

9. The GNSS-based equatorial plasma bubble detection method according to claim 7, characterized in that: The process of determining the start and end time of the equatorial plasma bubble sequence based on the TEC sequence of the detrended term and performing linear fitting on the start and end time includes: Define a straight line , for any two points on the line and , calculate the slope of the straight line y, the expression is: ； According to the slope m, calculate the intercept of the straight line y, the expression is: ； Based on the slope m and the intercept b, the linear fitting equation is obtained, which is expressed as follows: ； Here, y represents a straight line in the TEC data, and x represents the time data.

10. A GNSS-based equatorial plasma bubble detection system, characterized in that: The system comprises: A receiving module, used for receiving GNSS observation files transmitted by satellites; A processing module, used for extracting pseudo-range data from the data in the GNSS observation file, and extracting TEC data from the GNSS observation file according to the extracted pseudo-range data; The processing module is also used to extract the equatorial plasma bubble sequence based on the extracted TEC data; A detection module, used for performing equatorial plasma bubble detection according to the equatorial plasma bubble sequence to determine the equatorial plasma bubble event; Among them, TEC data is the total electron content calculated using GNSS observation files.

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