Multi-index combined high-credibility ionospheric scintillation monitoring method and system

Abstract

The invention discloses a multi-index combined high-credibility ionospheric scintillation monitoring system, which belongs to the field of ionospheric scintillation monitoring, and comprises the following steps: calculating an ionospheric scintillation intensity index based on historical observation data of a base station; calculating a flicker intensity threshold value in each satellite elevation angle interval according to the relative distribution condition of the ionospheric flicker intensity index and the satellite elevation angle and in combination with the phase flicker intensity index and the amplitude flicker intensity index of all satellites; respectively training ROTI, DI and AATR in the ionospheric scintillation intensity index to obtain respective random forest classifiers, and finally obtaining ionospheric scintillation intensity and scintillation intensity probability; and based on historical observation data of the base station, the observation value residual error change rate of all satellites is calculated, and the ionospheric scintillation intensity and probability are subjected to credibility evaluation. According to the method, the current ionospheric scintillation intensity is judged by using a random forest method, the intensity probability is given according to the judgment result, and credibility information is provided for a user.

Description

Technical Field

[0001] This invention belongs to the field of ionospheric scintillation monitoring, specifically relating to a highly reliable ionospheric scintillation monitoring method and system that combines multiple indicators. Background Technology

[0002] Ionospheric disturbances refer to rapid and significant changes in the ionosphere's internal physical characteristics, such as electron density and ion composition, caused by multiple factors. Small-scale ionospheric disturbances are mainly ionospheric scintillation caused by plasma bubbles, with structures ranging from tens of meters to tens of kilometers in size and short duration. However, during the disturbance period, the electron density in the ionosphere in this region changes by tens or even hundreds of times, interfering with the amplitude and phase stability of signals. This severely affects the receiver's tracking of BeiDou satellite signals, causing cycle slips or even loss of lock, resulting in positioning errors at the meter level. Currently, large-scale ionospheric scintillation monitoring methods mainly involve using a monitoring network composed of one or more devices, such as a dedicated ionospheric scintillation monitor receiver (ISMR), a general-purpose GNSS receiver, an ionospheric plumb line, satellite-based incoherent scattering radar, and ground-based radar, to detect the ionosphere and search for regions of abrupt ionospheric changes. The phase scintillation intensity index can be calculated using ISMR. and amplitude flicker intensity index ISMR directly monitors the impact of ionospheric scintillation on the phase and amplitude of receiver signals. National and regional institutions such as the China Space Environment Ground-Based Integrated Monitoring Network, the National Space Science Center of the Chinese Academy of Sciences, the Australian Space Environment Forecasting Centre, the Brazilian Ionospheric Anomaly Monitoring Network, the European Ionospheric Monitoring Network, and the Italian National Institute of Geophysics and Volcanology have established ionospheric monitoring networks using ISMR to monitor and study areas prone to ionospheric scintillation, aiming to reduce its impact on production and daily life.

[0003] The earliest proposed index for monitoring ionospheric scintillation intensity obtainable by a universal receiver was the Rate of Change of Total Electron Content in the Ionosphere (ROTI). Subsequently, several scholars proposed different ionospheric scintillation monitoring indices, including the Along-Arc TEC Rate (AATR) and the Carrier-to-Noise Ratio (S4) index. The Doppler index (DI) and other indices are used to characterize ionospheric scintillation intensity by separating the ionospheric delay in the GNSS signal and calculating its variation intensity within a certain window.

[0004] In the process of flickering event detection, it is currently generally considered that when 0.2 < Weak flickering occurs when <0.4. Strong flickering occurs when >0.6, but when When the value is greater than 0.4, the dynamic precise single-point positioning error will exceed 0.8m, severely impacting user positioning applications. Furthermore, due to factors such as satellite elevation angle, solar activity, and plasma bubble movement, it is impossible to accurately determine the current ionospheric scintillation state using a fixed threshold. Since the ROTI index cannot definitively quantify whether ionospheric scintillation has occurred, many scholars have chosen to use machine learning methods to classify scintillation. However, these methods primarily focus on using ISMR receiver observations, while research on scintillation discrimination using observations from general-purpose receivers is relatively limited. Summary of the Invention

[0005] This invention addresses the problems of high equipment cost and low deployment density in ionospheric scintillation monitoring using devices such as ISMR, vertical measuring instruments, and radar. It utilizes relatively low-cost, numerous, and widely distributed general-purpose GNSS receivers for ionospheric scintillation monitoring. However, the ROTI (Radio Occurrence Indication) obtained using general-purpose GNSS receivers is... Parameters such as ROTI and AATR suffer from problems such as inconsistent standards for scintillation monitoring intensity, inconsistent monitoring results, and lack of reliable references for monitoring results. This paper proposes a high-reliability ionospheric scintillation monitoring method that combines multiple indicators, including ROTI, DI, and AATR. Indices such as DI and AATR are used to determine the current ionospheric scintillation intensity using the random forest method, and the intensity probability is given according to the determination result to provide users with credibility information.

[0006] According to one aspect of the present invention, a high-confidence ionospheric scintillation monitoring method combining multiple indicators is provided, comprising:

[0007] Based on the historical observation data of the acquired reference stations, the ionospheric scintillation index of all satellites was calculated, and the phase scintillation index and amplitude scintillation index of the satellites obtained from all dedicated ionospheric scintillation receivers were also acquired.

[0008] Based on the relative distribution of the ionospheric scintillation intensity index and the satellite elevation angle of all satellites, and combined with the phase scintillation intensity index and amplitude scintillation intensity index of all satellites, the scintillation intensity threshold within each satellite elevation angle interval is calculated.

[0009] Based on the scintillation intensity threshold within each satellite elevation angle interval, the ROTI index in the ionospheric scintillation intensity index is analyzed. DI and AATR are trained separately to obtain their respective random forest classifiers. Finally, the output results of all random forest classifiers are summarized to obtain the ionospheric scintillation intensity and scintillation intensity probability.

[0010] Based on the historical observation data of the acquired reference station, the residual change rate of observation values ​​of all satellites is calculated, and the credibility of the output ionospheric scintillation intensity and scintillation intensity probability is evaluated. The ionospheric scintillation intensity and the credibility evaluation results are used together as the scintillation monitoring results.

[0011] Furthermore, the method also includes:

[0012] By using real-time updated satellite orbits, satellite clock errors, and pseudorange residual corrections, the rate of change of satellite observation residuals is calculated in real time. This rate of change is then cross-compared with the output ionospheric scintillation intensity. Based on the cross-comparison results, the classification criteria for scintillation intensity thresholds are adjusted, and the random forest classifier is dynamically updated.

[0013] Further, the scintillation intensity threshold within each satellite elevation angle interval is calculated, including:

[0014] Based on the ROTI index of ionospheric scintillation intensity, DI is classified according to satellite elevation angle, and the median and absolute median deviation are calculated for each satellite elevation angle interval under each scintillation index.

[0015] Different absolute median deviation multipliers were selected as ionospheric scintillation thresholds for different intensities.

[0016] Furthermore, calculating the scintillation intensity threshold within each satellite elevation angle interval also includes:

[0017] Based on the AATR in the ionospheric scintillation intensity index, it is mapped to the amplitude scintillation intensity index. Specifically, the AATR corresponding to weak scintillation is denoted as the first dataset, and the AATR corresponding to strong scintillation is denoted as the second dataset.

[0018] The median of the first dataset was used as the threshold for weak flickering of the AATR, and the median of the second dataset was used as the threshold for strong flickering of the AATR.

[0019] Furthermore, the ROTI index in the ionospheric scintillation intensity index was analyzed. DI and AATR were trained separately to obtain their respective random forest classifiers, including:

[0020] ROTI in the ionospheric scintillation index ROTI and DI are used as input parameters, respectively, along with local time, satellite elevation angle, and geomagnetic latitude of the puncture point. The scintillation intensity corresponding to the scintillation intensity threshold within each satellite elevation angle interval is used as the output parameter. Through training, ROTI and DI are obtained. The random forest classifiers corresponding to DI;

[0021] Using the AATR in the ionospheric scintillation index, local time, and geomagnetic latitude of the reference station as input parameters, and the scintillation intensity corresponding to the scintillation intensity thresholds in the first and second datasets as output parameters, a random forest classifier for AATR is obtained through training.

[0022] Furthermore, the rate of change of observation residuals for all satellites is calculated, including:

[0023] Based on the historical observation data obtained from the base station, the observation residuals of all satellites are calculated;

[0024] Based on the observation residuals of all satellites, after correcting for the influence of satellite elevation angles, the rate of change of observation residuals is calculated using the residuals of adjacent epochs.

[0025] Furthermore, a reliability assessment is performed on the output ionospheric scintillation intensity and scintillation intensity probability, including:

[0026] The median and standard deviation of the residual change rate of observed values ​​at different ionospheric scintillation intensities in the statistical data are used to compare the model output with historical statistical values.

[0027] The probability of flickering is calculated based on the cumulative distribution function of the normal distribution as the confidence assessment result, and it is used together with the flicker intensity as the flicker monitoring result.

[0028] Furthermore, the method also includes:

[0029] By using real-time updated satellite orbits, satellite clock errors, and pseudorange residual corrections, the rate of change of satellite observation residuals is calculated in real time. This rate of change is then cross-compared with the output ionospheric scintillation intensity. Based on the cross-comparison results, the classification criteria for scintillation intensity thresholds are adjusted, and the random forest classifier is dynamically updated.

[0030] According to one aspect of the present invention, a high-confidence ionospheric scintillation monitoring system combining multiple indicators is provided, comprising:

[0031] The scintillation intensity index acquisition module is used to calculate the ionospheric scintillation intensity index of all satellites based on the historical observation data of the acquired reference station, and at the same time acquire the phase scintillation intensity index and amplitude scintillation intensity index of all satellites obtained by the dedicated ionospheric scintillation receiver.

[0032] The scintillation intensity threshold calculation module is used to calculate the scintillation intensity threshold for each satellite within its elevation angle range, based on the relative distribution of the ionospheric scintillation intensity index and the satellite elevation angle of all satellites, combined with the phase scintillation intensity index and amplitude scintillation intensity index of all satellites.

[0033] The training and output module is used to perform scintillation intensity analysis on the ROTI (Radio Oscillator) index within each satellite elevation angle interval, based on the scintillation intensity threshold. DI and AATR are trained separately to obtain their respective random forest classifiers. Finally, the output results of all random forest classifiers are summarized to obtain the ionospheric scintillation intensity and scintillation intensity probability.

[0034] The scintillation monitoring module is used to calculate the residual change rate of observation values ​​of all satellites based on the historical observation data of the acquired reference station, to evaluate the credibility of the output ionospheric scintillation intensity and scintillation intensity probability, and to use the ionospheric scintillation intensity and credibility evaluation results together as the scintillation monitoring results.

[0035] According to one aspect of the present invention, an electronic device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the high-confidence ionospheric scintillation monitoring method with joint multi-index.

[0036] According to one aspect of the present invention, a computer-readable storage medium is provided that stores a computer program, which, when executed by a processor, implements the steps of the high-confidence ionospheric scintillation monitoring method with joint multi-index.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. This invention proposes a method for monitoring ionospheric scintillation by combining multiple indicators, which enables reliable assessment of ionospheric scintillation monitoring, solves the problems of inconsistent standards and inconsistent monitoring results of various indicators, and achieves the effect of highly reliable ionospheric scintillation monitoring and real-time uninterrupted optimization of monitoring parameters.

[0039] 2. This invention uses the random forest method to train and judge the observation data of the base station to obtain the ionospheric scintillation intensity and its probability, thereby improving the reliability of the information provided to users. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic diagram of a high-reliability ionospheric scintillation monitoring method that combines multiple indicators, as provided by the present invention. Detailed Implementation

[0042] 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 some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] like Figure 1 As shown, this invention proposes a high-confidence ionospheric scintillation monitoring method and system that combines multiple indicators, including: Step 1: Collecting historical observation data from reference stations within the region, sorting and removing anomalies from the observation data, and calculating the ROTI of all satellites for all available epochs. Data such as DI and AATR were collected to determine the phase scintillation intensity index output of the ISMR receiver at corresponding times and epochs. and amplitude flicker intensity index Step Two: Based on ROTI, The relative distribution of index data such as DI with satellite elevation angle, for reference. and Step 3: Calculate the scintillation intensity index threshold for each satellite elevation angle interval, and AATR calculates the scintillation intensity index threshold for the corresponding epoch. Step 4: Establish a random forest neural network model, using the corresponding index, local time, satellite elevation angle, and geomagnetic latitude of the puncture point as input parameters, and based on... and The determined flash intensity is used as the output parameter, and the joint ROTI is obtained through training. The model classifies scintillation intensity using indices such as ROTI and AATR, outputting scintillation intensity and corresponding probability. Step 4: The reliability of the scintillation intensity and probability output from Step 3 is assessed by calculating the rate of change of residuals from satellite observations at each reference station for the corresponding time period. Step 5: By continuously collecting ROTI, DI, and AATR indices... Using index data such as DI and AATR, and taking the real-time single-frequency single-point positioning error of the reference station as a reference, the scintillation intensity threshold division standard for different satellite elevation angle intervals in step two is adjusted, and the model in step three is iteratively updated.

[0044] Specifically, the embodiments of the present invention provide the specific operations for step one:

[0045] First, observations from general-purpose GNSS receivers at all reference stations in the region over the past three months were collected. The epoch-to-epoch differences in pseudorange, carrier, and Doppler observations for each satellite within a single arc were calculated. Anomalies in the epoch-to-epoch differences were checked, and after removing abnormal data, the phase scintillation index of the corresponding satellite output from ISMR receivers in the region corresponding to epochs without anomalies was collected. and amplitude flicker intensity index .

[0046] Then, ROTI is calculated using observations from a general-purpose GNSS receiver. Indices such as DI and AATR. The ROTI calculation method is as follows: geometrically inversely combined, the dual-frequency (frequency points i and j) carrier observations of satellite s from the same reference station r. The formula is as follows:

[0047] (1)

[0048] In the formula, This represents the carrier wave observation value at the i-th frequency point obtained by the reference station r during the tracking of satellite s. Indicates carrier wavelengths without geometric combinations. This represents the observations of the reference station r without geometric combination carrier waves obtained during the tracking of satellite s. Indicates the carrier wavelength at frequency point i. The carrier wavelength at frequency point j is represented. , The reference frequency for the ionosphere. Let i be the frequency of frequency point i. This represents the first-order ionospheric delay at the reference frequency during the tracking process of satellite s by reference station r. The impact of higher-order delays on the positioning results is ignored here. This represents the integer ambiguity of the carrier observation at frequency point i when the reference station r tracks satellite s. and Let r and s represent the carrier hardware delays of the reference station r and satellite s, respectively. Frequency j has the same meaning as frequency i. The ionospheric rate of change (ROT) can be obtained from the single difference between epochs in equation (1), as shown in equation (2):

[0049] (2)

[0050] In the formula, This indicates a single difference between epochs of observations without geometric combination carriers. and The epochal single difference represents the non-integer ambiguity. This represents the time difference between two epoch observations. Within the same arc segment, the integer part of the ambiguity remains unchanged. Furthermore, it is assumed that the non-integer ambiguity changes smoothly over a short period; therefore, the single difference between epochs for the integer ambiguity part is 0. and It is approximately 0. ROTI can be calculated according to equation (3), as follows:

[0051] (3)

[0052] Generally, ROTI within a 60s time window is calculated using 1Hz high-frequency GNSS observations, so the unit is TECU / min. The index is calculated by converting the signal-to-noise ratio (SNR) of BeiDou satellite signals into signal strength. (Satellite signal carrier-to-noise ratio (SNR)) The signal-to-noise ratio (SNR) is the ratio of the carrier signal power captured by the receiver's RF front-end to the noise power after demodulation by the baseband demodulator, expressed in decibels (dB), as follows:

[0053] (4)

[0054] In the formula, S represents the signal power, and N represents the signal noise power, typically taken as the average over a certain period. Since the carrier power in BeiDou signals is much smaller than the signal transmission power, therefore... It is very close to SNR in numerical value. (Through...) After calculating the signal strength, we obtain The exponent, formula as follows:

[0055] (5)

[0056] Where 〈 〉 indicates calculating the mean of the data sequence.

[0057] DI index calculation method: The Doppler observation equation of Beidou satellite is as shown in equation (6):

[0058] (6)

[0059] In the formula, This represents the Doppler observation value of the i-th frequency signal obtained by the reference station r during the tracking of satellite s; Let be the true rate of change of the distance between the Earth and the planet, and c be the speed of light (299,792,458.0 m / s). and These represent the rate of change of the base station and the satellite clock bias, respectively. This represents the rate of change of the first-order ionospheric delay at the reference frequency during the tracking process of satellite s by reference station r. The impact of higher-order delays on the positioning results is ignored here. Let be the tropospheric projection function from the vertical direction to the oblique path direction when the base station r tracks satellite s. This represents the rate of change of the total tropospheric delay above the reference station r. The noise in the Doppler observations is represented by the symbols in equation (1), and the remaining symbols have the same meaning. The Doppler observations at two frequency points i and j are combined into a geometrically unaffected observation set, and the ionospheric rate of change is calculated as follows:

[0060] (7)

[0061] In the formula, and These are the carrier wavelength without geometric combination and the Doppler observation value, respectively. Let represent the Doppler observation value of the j-th frequency signal obtained by the reference station r during the tracking of satellite s. Then, DI is obtained by calculating the standard deviation of the ionospheric change rate, i.e.:

[0062] (8)

[0063] In the formula, The symbol represents the mean of a series over a period of time; here, a 60-second observation series is used to calculate DI.

[0064] AATR represents the degree of ionospheric variation for all visible satellites in the region, calculated as shown in equation (9):

[0065] (9)

[0066] In the formula, el is the satellite elevation angle, sat represents all visible satellites in the area, and T is the start time of the time period. To calculate the time period length, n is the number of data points involved in this calculation. The ROT calculation method is shown in equation (2).

[0067] Specifically, the embodiments of the present invention provide the specific steps of step two:

[0068] According to ROTI, The relative distribution of index data such as DI with satellite elevation angle, for reference. and The scintillation intensity index threshold for each satellite elevation angle interval is calculated, taking ROTI as an example. The ROTI index is classified according to the satellite elevation angle, with each degree representing a satellite elevation angle interval. The median absolute deviation (MAD) of the ROTI index within each interval is calculated, as shown in Equation (10). By selecting different MAD multiplication factors as the ionospheric scintillation thresholds of different intensities, it is considered that no scintillation occurs when the MAD is less than 6 times, weak scintillation occurs when the MAD is greater than 6 times the standard deviation but less than 12 times, and strong scintillation occurs when the MAD is greater than 12 times.

[0069] (10)

[0070] In the formula, X is the residual dataset. For samples in X, This represents the inverse cumulative distribution function. The method for determining this is... , median represents the median, and a is the corresponding coefficient, which is 6 or 12 here. After obtaining each satellite elevation angle interval, that is, the strong scintillation and weak scintillation thresholds corresponding to 1 degree satellite elevation angle, the function is fitted using equation (11):

[0071] (11)

[0072] In the formula, A, B, C, and D are the coefficients to be fitted, x is the satellite elevation angle, and f is the threshold for strong or weak flicker. Once this fitting function is obtained, the presence and intensity of flickering by the satellite can be determined based on the satellite elevation angle.

[0073] AATR directly with To correspond, when 0.2 < A value less than 0.4 is considered weak flickering. The corresponding AATR value is then used to construct dataset A. Strong flickering occurs when the value is greater than 0.6. Find the corresponding AATR value to form dataset B. Calculate the median of A and B respectively as the threshold for weak and strong flickering of AATR.

[0074] Specifically, the embodiments of the present invention provide specific operation steps for step three:

[0075] Organize the data obtained in steps one and two, starting with ROTI, Training with parameters such as DI is illustrated using ROTI as an example. For each satellite observation, the local time, satellite elevation angle, geomagnetic latitude of the puncture point, ROTI, and ionospheric scintillation intensity calculated in step two are mapped one-to-one. If data for a particular satellite at a given epoch is missing, the data for that epoch is removed. After sorting by local time, a random forest classifier is trained. The training inputs are local time, satellite elevation angle, geomagnetic latitude of the puncture point, and ROTI index. The model output is ionospheric scintillation intensity and its probability. For AATR data, the local time and geomagnetic latitude and longitude of the base station are used as input data, and the ionospheric scintillation intensity calculated in step two is used as output to train the random forest classifier. Finally, the random forest classifier summarizes all ROTI values. The four classifiers, DI and AATR, output the final ionospheric scintillation intensity and probability.

[0076] Specifically, in this embodiment of the invention, the reliability of the scintillation intensity and probability output by the model in step three is evaluated by calculating the rate of change of the residuals of satellite observations at each reference station during the corresponding time period. The satellite observation residuals are calculated using the historical observation data of the reference stations collected in step one, and the observation model is shown in equation (12).

[0077] (12)

[0078] In the formula, s, r, and i represent the satellite, reference station, and frequency point number, respectively; This represents the pseudorange observation value of the i-th frequency signal obtained by the base station r during the tracking of satellite s; Let c be the true value of the distance between the Earth and the planet, and c be the speed of light (299,792,458.0 m / s). and These represent the clock bias of the base station and the satellite, respectively. Reference frequency for the ionosphere With frequency i The square of the ratio, This represents the first-order ionospheric delay at the reference frequency during the tracking process of satellite s by reference station r. The impact of higher-order delays on the positioning results is ignored here. Let be the tropospheric projection function from the vertical direction to the oblique path direction when the base station r tracks satellite s. This represents the total tropospheric delay above the base station r. and These represent the pseudorange hardware delays of the base station and the satellite, respectively. This indicates the multipath delay error received by the base station r from the satellite s. This represents noise in pseudorange observations.

[0079] The satellite orbit, satellite clock error, and pseudorange hardware delay products are calculated using precise ephemeris to correct the observed values. The tropospheric delay error is calculated using the standard atmosphere and Saastamoninen models and corrected in the observed values. The known base station coordinates and the above-corrected observed values ​​are substituted into equation (12) to obtain the satellite observation residuals. After correcting for the influence of the satellite elevation angle, the rate of change is calculated using the residuals of adjacent epochs, as shown in equation (13).

[0080] (13)

[0081] In the formula, d(vRes) is the rate of change of the residuals, and Res is the observed residuals calculated above. For observation time, The constant coefficient is 0.9782, R is the Earth's radius, H is the height of the shell (506.7 km), and E1 is the satellite's elevation angle. Represents the projection function. This represents the residual of satellite observations after correcting for the influence of satellite elevation angle.

[0082] Specifically, the median and standard deviation of the residual change rate when ionospheric scintillation of different intensities occurs in the statistical data (residual change rate calculation). The output of the random forest classifier is compared with historical statistical values ​​(median and standard deviation of the residual change rate when ionospheric scintillation of corresponding intensities occurs in historical data). The probability of scintillation is calculated based on the cumulative distribution function of the normal distribution as the external credibility assessment result. This result, together with the scintillation intensity output by the model, is provided to the user as the credibility of the scintillation monitoring result.

[0083] Specifically, the embodiments of the present invention provide specific operation steps for step five:

[0084] As the model continues to run and data accumulates, it is continuously updated based on the data of the current region. First, the precise ephemeris used in calculating the residuals in step four is replaced with real-time updated satellite orbits, satellite clock errors, and pseudorange residual corrections. The rate of change of the residuals of real-time satellite observations is calculated. Then, it is cross-compared with the ionospheric scintillation intensity output in step three. The scintillation intensity classification threshold is updated based on the inconsistent data in the cross-comparison results, and the random forest classifier in step three is updated online.

[0085] The implementation of the various embodiments of the present invention is based on programmed processing by a device with processor functionality. Therefore, in practical engineering, the technical solutions and functions of the various embodiments of the present invention are encapsulated into various modules. Based on this reality, and building upon the above embodiments, the embodiments of the present invention provide a high-confidence ionospheric scintillation monitoring system with multiple joint indicators. This system is used to execute a high-confidence ionospheric scintillation monitoring method with multiple joint indicators as described in the above method embodiments.

[0086] The system includes: a scintillation intensity index acquisition module, used to calculate the ionospheric scintillation intensity index of all satellites based on historical observation data from the acquired reference station, and simultaneously acquire the phase scintillation intensity index and amplitude scintillation intensity index of all satellites obtained from a dedicated ionospheric scintillation receiver; a scintillation intensity threshold calculation module, used to calculate the scintillation intensity threshold within each satellite elevation angle interval based on the relative distribution of the ionospheric scintillation intensity index of all satellites and the satellite elevation angle, combined with the phase scintillation intensity index and amplitude scintillation intensity index of all satellites; and a training and output module, used to train and output the ROTI (Radio Oscillator Indices) of the ionospheric scintillation intensity index based on the scintillation intensity threshold within each satellite elevation angle interval. The system trains DI and AATR separately to obtain their respective random forest classifiers. Finally, the output results of all random forest classifiers are summarized to obtain the ionospheric scintillation intensity and scintillation intensity probability. The scintillation monitoring module is used to calculate the residual change rate of observation values ​​of all satellites based on the historical observation data of the acquired reference station, and to evaluate the credibility of the output ionospheric scintillation intensity and scintillation intensity probability. The ionospheric scintillation intensity and the credibility evaluation results are used together as the scintillation monitoring results.

[0087] The high-reliability ionospheric scintillation monitoring system provided in this invention addresses the problems of high equipment cost and low deployment density associated with using ISMR, vertical measuring instruments, and radar for ionospheric scintillation monitoring. It utilizes relatively low-cost, numerous, and widely distributed general-purpose GNSS receivers for ionospheric scintillation monitoring. However, the ROTI obtained using general-purpose GNSS receivers... Parameters such as DI and AATR suffer from issues such as inconsistent standards for scintillation monitoring intensity, inconsistent monitoring results, and lack of reliable references for monitoring results. Therefore, several modules are employed, combined with ROTI, Indices such as DI and AATR are used to determine the current ionospheric scintillation intensity using the random forest method, and the intensity probability is given according to the determination result, providing users with credibility information and improving the accuracy of the ionospheric model.

[0088] Based on the same inventive concept as the foregoing embodiments, this embodiment of the invention also provides an electronic device, including a memory and a processor. The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize a high-confidence ionospheric scintillation monitoring method with multiple joint indicators as proposed in the above embodiments.

[0089] This invention also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, this program solves problems such as inconsistent standards for flicker monitoring intensity, inconsistent monitoring results, and lack of reliable reference for monitoring results.

[0090] Finally, it should be noted that the above specific embodiments are merely representative examples of the present invention. Obviously, the present invention is not limited to the above specific embodiments and many variations are possible. Any simple modifications, equivalent changes, and alterations made to the above specific embodiments based on the technical essence of the present invention should be considered within the protection scope of the present invention.

Claims

1. A high-confidence ionospheric scintillation monitoring method combining multiple indicators, characterized in that, include: Based on the historical observation data of the acquired reference stations, the ionospheric scintillation index of all satellites was calculated, and the phase scintillation index and amplitude scintillation index of the satellites obtained from all dedicated ionospheric scintillation receivers were also acquired. Based on the relative distribution of the ionospheric scintillation intensity index and the satellite elevation angle of all satellites, and combined with the phase scintillation intensity index and amplitude scintillation intensity index of all satellites, the scintillation intensity threshold within each satellite elevation angle interval is calculated. Based on the scintillation intensity threshold within each satellite elevation angle interval, the ROTI index in the ionospheric scintillation intensity index is analyzed. DI and AATR are trained separately to obtain their respective random forest classifiers. Finally, the output results of all random forest classifiers are summarized to obtain the ionospheric scintillation intensity and scintillation intensity probability. Based on the historical observation data of the acquired reference station, the residual change rate of observation values ​​of all satellites is calculated, and the credibility of the output ionospheric scintillation intensity and scintillation intensity probability is evaluated. The ionospheric scintillation intensity and the credibility evaluation results are used together as the scintillation monitoring results.

2. The high-confidence ionospheric scintillation monitoring method combining multiple indicators according to claim 1, characterized in that, The method further includes: By using real-time updated satellite orbits, satellite clock errors, and pseudorange residual corrections, the rate of change of satellite observation residuals is calculated in real time. This rate of change is then cross-compared with the output ionospheric scintillation intensity. Based on the cross-comparison results, the classification criteria for scintillation intensity thresholds are adjusted, and the random forest classifier is dynamically updated.

3. The high-confidence ionospheric scintillation monitoring method combining multiple indicators according to claim 1, characterized in that, Calculate the scintillation intensity threshold within each satellite elevation angle interval, including: Based on the ROTI index of ionospheric scintillation intensity, DI, respectively, are classified according to satellite elevation angle, and the median and absolute median deviation are calculated for each satellite elevation angle interval under each scintillation index; Different absolute median deviation multipliers were selected as ionospheric scintillation thresholds for different intensities.

4. The high-confidence ionospheric scintillation monitoring method combining multiple indicators according to claim 1, characterized in that, Calculating the scintillation intensity threshold within each satellite elevation angle interval also includes: Based on the AATR in the ionospheric scintillation intensity index, it is mapped to the amplitude scintillation intensity index. Specifically, the AATR corresponding to weak scintillation is denoted as the first dataset, and the AATR corresponding to strong scintillation is denoted as the second dataset. The median of the first dataset was used as the threshold for weak flickering of the AATR, and the median of the second dataset was used as the threshold for strong flickering of the AATR.

5. The high-confidence ionospheric scintillation monitoring method combining multiple indicators according to claim 4, characterized in that, ROTI in the ionospheric scintillation intensity index DI and AATR were trained separately to obtain their respective random forest classifiers, including: ROTI in the ionospheric scintillation index ROTI and DI are used as input parameters, respectively, along with local time, satellite elevation angle, and geomagnetic latitude of the puncture point. The scintillation intensity corresponding to the scintillation intensity threshold within each satellite elevation angle interval is used as the output parameter. Through training, ROTI and DI are obtained. The random forest classifiers corresponding to DI; Using the AATR in the ionospheric scintillation index, local time, and geomagnetic latitude of the reference station as input parameters, and the scintillation intensity corresponding to the scintillation intensity thresholds in the first and second datasets as output parameters, a random forest classifier for AATR is obtained through training.

6. The high-confidence ionospheric scintillation monitoring method combining multiple indicators according to claim 1, characterized in that, Calculate the rate of change of observation residuals for all satellites, including: Based on the historical observation data obtained from the base station, the observation residuals of all satellites are calculated; Based on the observation residuals of all satellites, after correcting for the influence of satellite elevation angles, the rate of change of observation residuals is calculated using the residuals of adjacent epochs.

7. The high-confidence ionospheric scintillation monitoring method combining multiple indicators according to claim 1, characterized in that, The reliability of the output ionospheric scintillation intensity and scintillation intensity probability is evaluated, including: The median and standard deviation of the residual change rate of observed values ​​at different ionospheric scintillation intensities in the statistical data are used to compare the model output with historical statistical values. The probability of flickering is calculated based on the cumulative distribution function of the normal distribution as the confidence assessment result, and it is used together with the flicker intensity as the flicker monitoring result.

8. A high-reliability ionospheric scintillation monitoring system combining multiple indicators, characterized in that, include: The scintillation intensity index acquisition module is used to calculate the ionospheric scintillation intensity index of all satellites based on the historical observation data of the acquired reference station, and at the same time acquire the phase scintillation intensity index and amplitude scintillation intensity index of all satellites obtained by the dedicated ionospheric scintillation receiver. The scintillation intensity threshold calculation module is used to calculate the scintillation intensity threshold for each satellite within its elevation angle range, based on the relative distribution of the ionospheric scintillation intensity index and the satellite elevation angle of all satellites, combined with the phase scintillation intensity index and amplitude scintillation intensity index of all satellites. The training and output module is used to perform scintillation intensity analysis on the ROTI (Radio Oscillator) index within each satellite elevation angle interval, based on the scintillation intensity threshold. DI and AATR are trained separately to obtain their respective random forest classifiers. Finally, the output results of all random forest classifiers are summarized to obtain the ionospheric scintillation intensity and scintillation intensity probability. The scintillation monitoring module is used to calculate the residual change rate of observation values ​​of all satellites based on the historical observation data of the acquired reference station, to evaluate the credibility of the output ionospheric scintillation intensity and scintillation intensity probability, and to use the ionospheric scintillation intensity and credibility evaluation results together as the scintillation monitoring results.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the high-confidence ionospheric scintillation monitoring method with joint multiple indicators as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the high-confidence ionospheric scintillation monitoring method with joint multiple indicators as described in any one of claims 1 to 7.

Images