CN113625356A - Real-time anomaly monitoring method suitable for single-station ionized layer TEC - Google Patents

Real-time anomaly monitoring method suitable for single-station ionized layer TEC Download PDF

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CN113625356A
CN113625356A CN202110758077.5A CN202110758077A CN113625356A CN 113625356 A CN113625356 A CN 113625356A CN 202110758077 A CN202110758077 A CN 202110758077A CN 113625356 A CN113625356 A CN 113625356A
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黄智�
张娜娜
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Abstract

A real-time anomaly monitoring method suitable for a single-station ionized layer TEC (thermoelectric cooler) comprises the steps of firstly obtaining a global mapping file provided by a European orbit determination center CODE (CODE), and obtaining the ionized layer vertical TEC at the position where an observation single station is located by utilizing spherical harmonic expansion and inclination factors; setting window length, subsequence length, main component number and error threshold parameters, selecting TEC historical time sequences of solar activity and geomagnetic activity in quiet periods, taking abnormal space precursors such as magnetic storm and the like as sequences to be monitored, performing singular value decomposition by using Lanczos and QR, and adjusting parameters to determine an optimal solution according to abnormal monitoring accuracy and false detection rate; and carrying out real-time TEC sequence data flow monitoring by utilizing the optimized parameters. The method can improve the accuracy and the real-time performance of ionized layer TEC anomaly identification, provide high-quality service for a satellite-ground radio wave propagation engineering system, facilitate the construction of scientific and reasonable space environment anomaly monitoring, and enrich the technology of forecasting the earthquake in the middle and short term before the earthquake.

Description

Real-time anomaly monitoring method suitable for single-station ionized layer TEC
Technical Field
The invention relates to an ionized layer TEC anomaly monitoring method, in particular to a real-time anomaly monitoring method suitable for a single-station ionized layer TEC, and belongs to the technical field of ionized layer space environment forecasting.
Background
The ionosphere 60-1000km away from the earth surface contains a large amount of ions and free electrons, and can reflect or refract radio waves passing through the ionosphere under the comprehensive action of solar rays, cosmic rays and other settled ions, so that additional time delay is generated on a spatial information transmission link, and the service performance of various spatial application systems such as remote sensing and remote measuring, navigation and positioning, over-the-horizon radar and the like is reduced. The ionospheric additional delay can be attributed to the total concentration of electrons TEC in the satellite-receiver line-of-sight direction. Therefore, the elimination of the additional time delay of the ionized layer provides high-quality service for the engineering technology of satellite-ground radio wave propagation, and the real-time determination of the total content TEC of the electron concentration is always an important problem in the field of space environment.
In the early stage, the time and space changes of the total electronic concentration content TEC can be obtained by utilizing the ground observation technology of the radio beacon, such as faraday rotation and differential doppler frequency shift, the global positioning system GNSS is continuously put into use, the measurement of the total electronic concentration content TEC by utilizing GNSS dual-frequency observation data is rapidly developed and becomes the mainstream technical means for extracting the total electronic concentration content TEC, but the limitation of conditions such as rivers, snow regions, polar regions and the like possibly causes that stations cannot be distributed in some regions, the ionosphere morphological description in local regions or a certain period of time is easy to lack, and ionosphere refraction correction parameters cannot be provided for a space application system. Therefore, establishing an appropriate single-station or regional ionosphere empirical model is a hot problem for researchers at home and abroad. The most famous Ionosphere empirical model is the International Reference Ionosphere (IRI), and the IRI model integrates along the height according to an Ionosphere electron concentration profile given by an empirical mode to obtain the required Ionosphere TEC. The model has high running speed and certain forecasting capability, and can accurately describe the time-space change characteristics of the total electron concentration TEC in the global range, but if a certain area or a certain condition such as extremely low/extremely large solar activity data is lacked, the output error is obviously increased, compared with the total electron concentration TEC mode calculated by indirect data, the single-station TEC empirical mode established by utilizing the total electron concentration TEC observation data per se and based on the technologies of Fourier series expansion, harmonic analysis, empirical orthogonal function EOF expansion and the like generally has higher precision, but the Fourier expansion technology or the empirical orthogonal function EOF reconstruction needs to preset a basis function, has a certain deviation on the real form description of the total electron concentration TEC, and needs a plurality of equations.
In recent years, artificial intelligence technology is rapidly developed, and as a neural network can well describe a complex nonlinear input-output relationship, more and more scholars apply the neural network to the field of ionosphere TEC parameter modeling. The precision of the TEC model based on the neural network is further improved, but the model structure is often complex, the parameters are difficult to find the optimal solution, the running time is long, and the sun blackson number, the solar radiation flux F10.7 index, the geomagnetic activity index, the Dst index of the change of the ring current during the magnetic storm and the like which are used as the input layer parameters of the neural network also need to be updated in real time, so that the established TEC prediction model is difficult to further popularize and implement in practical application, particularly, the TEC of the ionized layer is likely to have a sudden change before the geomagnetic activity is obviously enhanced or the earthquake occurs, the space-time gradient change is large, and the construction of the high-precision TEC real-time model is very difficult and challenging.
When space environment abnormal events such as magnetic storm occur, the output precision of the model is generally reduced under the condition that the representation capability of the existing model is limited, so that a plurality of scholars develop research aiming at ionized layer TEC abnormal monitoring. The traditional mathematical statistics method represented by the autoregressive moving average model ARIMA and the sliding quartering distance utilizes the statistical characteristics of normal activities to establish norm distribution, and utilizes statistical test to determine whether the observed TEC parameters are obviously deviated from the norm distribution. The principal component analysis PCA utilizes the magnitude of the principal characteristic value to carry out TEC abnormality screening, and has higher identification rate for premonitory space precursor abnormality of a major earthquake. The method provides abnormal spatial information such as magnetic storm, earthquake pre-earthquake and the like for various spatial systems which use radio as propagation beacons, such as remote sensing and remote measurement, navigation and positioning, and the like, so that the accuracy rate of judgment is required to be improved, the real-time performance is further improved, and the business of abnormal monitoring is really realized.
Disclosure of Invention
The invention aims to provide a real-time anomaly monitoring method for a single-station ionized layer TEC, which improves the accuracy and the real-time property of anomaly identification of the ionized layer TEC and provides high-quality service for a satellite-ground radio wave propagation engineering system.
In order to achieve the purpose, the invention provides a real-time anomaly monitoring method for a single-station ionized layer TEC, which comprises the following steps:
firstly, establishing an ionosphere vertical TEC spherical harmonic function space-time model, drawing a GIM file by a global map provided by a European orbit determination center CODE, reading expansion coefficients of the TEC spherical harmonic model, and giving a geographical latitude and a longitude of an observation single station to obtain a one-dimensional TEC time sequence;
selecting a TEC time sequence of a period when the solar activity and the geomagnetic activity are calm according to the sizes of the characteristic solar activity index F107 and the geomagnetic activity index Dst, defining the TEC time sequence as a TEC historical time sequence, and extracting the TEC historical time sequence s (t) by using a sliding window with the length of w: s (t) ═ si,si+1,...si+w-1)TWherein s isi,si+1,...si+w-1TEC observed values at the time i, i +1 and i + w-1 respectively, wherein T represents the transposition of the matrix;
at a reference time τ, the track matrix of the TEC historical time series is defined as Sq：
Figure BDA0003147963350000021
In the formula: k is the number of the sub-sequences of the track matrix, and the rightmost matrix with equal sign is(s)τ-k-w+1,…sτ-w) The expansion of (3);
at time t, the time sequence of the abnormal TEC to be monitored is Sa：
Figure BDA0003147963350000031
In the formula: the rightmost matrix of equal sign is(s)t-k-w+1,…st-w) The expansion of (3);
thirdly, a track matrix S of the TEC historical time sequenceqAnd a track matrix S of the abnormal TEC time sequence to be monitoredaConverting the data into a symmetric matrix, and performing singular value decomposition by using a Lanczos-QR combination method to obtain a characteristic value and an orthogonal vector:
S＝UWVT (3)
U＝(u1,u2,…,uk) Is the left singular orthogonal vector, V ═ V1,v2,…,vk)TIs the right singular orthogonal vector, W is the diagonal matrix eigenvalue λl，λ2，…λk；
Selecting historical time sequence SqThe first m eigenvectors U1m：
U1m＝(u1,u2,…,um) (4)
Similarly, the abnormal time series SaThe first m eigenvectors U2m：
U2m＝(μ1,μ2,…,μm) (5)；
Fifthly, measuring the track matrix S of the TEC historical time sequenceqAnd the time sequence of the abnormal TEC to be monitored is SaSimilarity between K: k (i, μ) ═ μTuiThen the anomaly score z (t) is defined as:
Figure BDA0003147963350000032
in the formula: i represents SqOr SaThe ith feature vector in the time series; determining the size of a threshold value by corresponding numerical values in the abnormal score sequence according to the occurrence condition of spatial abnormal precursors such as magnetic storms in the sequence to be monitored;
after the threshold is determined, selecting a time t, and extracting the current TEC time sequence by using a sliding window with the length of w: s (t) ═ si,si+1,...si+w-1)TThe historical TEC time sequence is defined as S1The TEC time sequence to be monitored is S2：
Figure BDA0003147963350000033
In the formula, gamma is a positive integer, the time lags behind historical data, the steps (i) - (c) are repeated, the calculated z (t) sequence is compared with a threshold value, and if the score value in z (t) is larger than a given threshold value, the occurrence and the occurrence time of the abnormal event are judged.
Further, in the third step, singular value decomposition adopts a Lanczos-QR combined method to solve eigenvalues, and obtains a tri-diagonal matrix T by successively tri-diagonalizing the symmetric matrix a, so that eigenvalues and eigenvectors of the tri-diagonal matrix T can be regarded as approximations of eigenvalues and eigenvectors of the symmetric matrix a, and the expression form of the tri-diagonal matrix T:
Figure BDA0003147963350000041
adopting Lanczos to iteratively solve alpha and beta variables in the formula (8), firstly, utilizing a power method to solve the abnormal TEC time sequence S to be monitoredaFirst normalized feature vector q1＝μ，
Figure BDA0003147963350000042
β0＝1，q01, calculate:
Figure BDA0003147963350000043
then i +1, repeating the formula (9) until i ═ ns, and sequentially calculating α1，β1，α2，β2，…，αns；
After the tri-diagonal matrix T is determined, a QR algorithm is adopted for characteristic decomposition, namely T is PXPTWhere P is the orthogonal eigenvector, X is the eigenvalue, P is initialized to the identity matrix, i is 1, T1T, calculate:
Figure BDA0003147963350000044
in the formula, R is a nonsingular upper triangular matrix;
and then i +1, repeating the formula (10) until the difference T-P is smaller than the threshold value 1e-3, finishing the iteration, taking the first m components of the feature vector, and calculating the abnormal score according to the formula (6).
Compared with the prior art, the method comprises the steps of firstly obtaining a global mapping file provided by a European orbit determination center CODE, and obtaining the ionosphere vertical TEC of the position where an observation single station is located by utilizing spherical harmonic expansion and a tilt factor; setting window length, subsequence length, main component number and error threshold parameters, selecting TEC historical time sequences of solar activity and geomagnetic activity in quiet periods, taking abnormal space precursors such as magnetic storm and the like as sequences to be monitored, performing singular value decomposition by using Lanczos and QR, and adjusting parameters to determine an optimal solution according to abnormal monitoring accuracy and false detection rate; and carrying out real-time TEC sequence data flow monitoring by utilizing the optimized parameters. The method can improve the accuracy and the real-time performance of ionized layer TEC (thermoelectric cooler) anomaly identification, is high in speed, provides high-quality service for a satellite-ground radio wave propagation engineering system, is beneficial to constructing scientific and reasonable space environment anomaly monitoring, and enriches the technology of forecasting the earthquake in the early, middle and short periods.
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FIG. 1 is a functional block diagram of the present invention;
fig. 2 is an example of 2005 TEC sequence monitoring at beijing station in an embodiment of the present invention.
Detailed Description
The invention will be further explained with reference to the drawings.
As shown in fig. 1, a real-time anomaly monitoring method for a single-station ionosphere TEC includes the following steps:
firstly, establishing an ionosphere vertical TEC spherical harmonic function space-time model, drawing a GIM file by a global map provided by a European orbit determination center CODE, reading expansion coefficients of the TEC spherical harmonic model, and giving a geographical latitude and a longitude of an observation single station to obtain a one-dimensional TEC time sequence;
② according to the large of the representation sun activity index F107 and geomagnetic activity index DstAnd when the current time sequence is small, selecting a TEC time sequence of a period when the solar activity and the geomagnetic activity are calm, defining the TEC time sequence as a TEC historical time sequence, and extracting the TEC historical time sequence s (t) by using a sliding window with the length of w: s (t) ═ si,si+1,...si+w-1)TWherein s isi,si+1,...si+w-1TEC observed values at the time i, i +1 and i + w-1 respectively, wherein T represents the transposition of the matrix;
at a reference time τ, the track matrix of the TEC historical time series is defined as Sq：
Figure BDA0003147963350000051
In the formula: k is the number of the sub-sequences of the track matrix, and the rightmost matrix with equal sign is(s)τ-k-w+1,...sτ-w) The expansion of (3);
at time t, the time sequence of the abnormal TEC to be monitored is Sa：
Figure BDA0003147963350000052
In the formula: the rightmost matrix of equal sign is(s)t-k-w+1,...st-w) The expansion of (3);
thirdly, a track matrix S of the TEC historical time sequenceqAnd a track matrix S of the abnormal TEC time sequence to be monitoredaConverting the data into a symmetric matrix, and performing singular value decomposition by using a Lanczos-QR combination method to obtain a characteristic value and an orthogonal vector:
S＝UWVT (3)
U＝(u1,u2,…,uk) Is the left singular orthogonal vector, V ═ V1,v2,…,vk)TIs the right singular orthogonal vector, W is the diagonal matrix eigenvalue λl，λ2，…λk；
Fourthly, most of information of the original sequence can be reconstructed by utilizing the first few main components due to singular spectrum analysis, and meanwhile, the real-time performance of the engineering technology is consideredSelecting a historical time sequence S according to sexual requirementsqThe first m eigenvectors U1m：
U1m＝(u1,u2,…,um) (4)
Similarly, the abnormal time series SaThe first m eigenvectors U2m：
U2m＝(μ1,μ2,…,μm) (5)；
Fifthly, measuring the track matrix S of the TEC historical time sequenceqAnd the time sequence of the abnormal TEC to be monitored is SaSimilarity between K: k (i, μ) ═ μTuiThen the anomaly score z (t) is defined as:
Figure BDA0003147963350000061
in the formula: i represents SqOr SaThe ith feature vector in the time series; determining the size of a threshold value by corresponding numerical values in the abnormal score sequence according to the occurrence condition of spatial abnormal precursors such as magnetic storms in the sequence to be monitored;
after the threshold is determined, selecting a time t, and extracting the current TEC time sequence by using a sliding window with the length of w: s (t) ═ si,si+1,...si+w-1)TThe historical TEC time sequence is defined as S1The TEC time sequence to be monitored is S2：
Figure BDA0003147963350000062
In the formula, gamma is a positive integer, the time lags behind historical data, the steps (i) - (c) are repeated, the calculated z (t) sequence is compared with a threshold value, and if the score value in z (t) is larger than a given threshold value, the occurrence and the occurrence time of the abnormal event are judged.
Furthermore, in the step (iii), there are many methods for eigenvalue decomposition in the formula (3), such as subspace iteration, Lanczos, Jacobi, QR decomposition, and the like. The engineering requirement is high in real-time performance, and considering the characteristics of Lanczos, such as high speed, high efficiency and stable and reliable QR, the Lanczos-QR combination method is adopted to calculate the characteristic value, the symmetric matrix A is subjected to successive three-diagonal angle to obtain the three-diagonal matrix T, and the characteristic value and the characteristic vector of the three-diagonal matrix T can be regarded as the approximation of the characteristic value and the characteristic vector of the symmetric matrix A, and the expression form of the three-diagonal matrix T is as follows:
Figure BDA0003147963350000071
adopting Lanczos to iteratively solve alpha and beta variables in the formula (8), firstly, utilizing a power method to solve the abnormal TEC time sequence S to be monitoredaFirst normalized feature vector q1＝μ，
Figure BDA0003147963350000072
β0＝1，q01, calculate:
Figure BDA0003147963350000073
then i +1, repeating the formula (9) until i ═ ns, and sequentially calculating α1，β1，α2，β2，…，αns；
After the tri-diagonal matrix T is determined, a QR algorithm is adopted for characteristic decomposition, namely T is PXPTWhere P is the orthogonal eigenvector, X is the eigenvalue, P is initialized to the identity matrix, i is 1, T1T, calculate:
Figure BDA0003147963350000074
in the formula, R is a nonsingular upper triangular matrix;
and then i +1, repeating the formula (10) until the difference T-P is smaller than the threshold value 1e-3, finishing the iteration, taking the first m components of the feature vector, and calculating the abnormal score according to the formula (6).
Examples
According to the geographical longitude and latitude (39.61 degrees N, 115.89 degrees E) of a Beijing station, calculating vertical TEC observation data of continuous 100 days in 1 month, 1 day, 4 months and 10 months in 2005 by using a spherical harmonic function expansion coefficient provided by a CODE GIM file, wherein the time resolution is 2 hours, and further converting the vertical TEC observation data into a one-dimensional time sequence; extracting a historical time sequence and a time sequence to be monitored by using a sliding window with the length of 12, wherein the lag time gamma of the sequence to be monitored is 6, and further converting the historical time sequence and the time sequence to be monitored into a track matrix; converting the track matrix of the historical time sequence and the time sequence to be monitored into a symmetric matrix, and performing singular value decomposition by using a Lanczos-QR (quick response) combination method; fourthly, taking the first 5 eigenvectors, calculating an abnormal score z (t) according to the fifth step, wherein the result is shown in fig. 2, providing a geomagnetic activity characterization index Dst according to WDC for Geomagnetism and Kyoto data center, wherein the Dst is about-90 nT when 22 days 1 and 21 months 2005 is taken, the Dst is gradually recovered after 24 days, the spatial environment is abnormal according to the size of the Dst index, and the calculated abnormal score is obviously larger and is about 0.0006; in addition, Dst was about-80 nT at 18/2/2005, and gradually recovered at 19/d, and the calculated abnormality score was significantly large, about 0.00048.

Claims (2)

1. A real-time anomaly monitoring method suitable for a single-station ionized layer TEC is characterized by comprising the following steps:
firstly, establishing an ionosphere vertical TEC spherical harmonic function space-time model, drawing a GIM file by a global map provided by a European orbit determination center CODE, reading expansion coefficients of the TEC spherical harmonic model, and giving a geographical latitude and a longitude of an observation single station to obtain a one-dimensional TEC time sequence;
selecting a TEC time sequence of a period when the solar activity and the geomagnetic activity are calm according to the sizes of the characteristic solar activity index F107 and the geomagnetic activity index Dst, defining the TEC time sequence as a TEC historical time sequence, and extracting the TEC historical time sequence s (t) by using a sliding window with the length of w: s (t) ═ si,si+1,...si+w-1)TWherein s isi,si+1,...si+w-1TEC observed values at the time i, i +1 and i + w-1 respectively, wherein T represents the transposition of the matrix;
at a reference time τ, the track matrix of the TEC historical time series is defined as Sq：
Figure FDA0003147963340000011
In the formula: k is the number of the sub-sequences of the track matrix, and the rightmost matrix with equal sign is(s)τ-k-w+1,...sτ-w) The expansion of (3);
at time t, the time sequence of the abnormal TEC to be monitored is Sa：
Figure FDA0003147963340000012
In the formula: the rightmost matrix of equal sign is(s)t-k-w+1,...st-w) The expansion of (3);
thirdly, a track matrix S of the TEC historical time sequenceqAnd a track matrix S of the abnormal TEC time sequence to be monitoredaConverting the data into a symmetric matrix, and performing singular value decomposition by using a Lanczos-QR combination method to obtain a characteristic value and an orthogonal vector:
S＝UWVT (3)
U＝(u1,u2,…,uk) Is the left singular orthogonal vector, V ═ V1,v2,…,vk)TIs the right singular orthogonal vector, W is the diagonal matrix eigenvalue λl，λ2，…λk；
Selecting historical time sequence SqThe first m eigenvectors U1m：
U1m＝(u1,u2,…,um) (4)
Similarly, the abnormal time series SaThe first m eigenvectors U2m：
U2m＝(μ1,μ2,…,μm) (5)；
Fifthly, measuring the track matrix S of the TEC historical time sequenceqAnd the time sequence of the abnormal TEC to be monitored is SaSimilarity between K: k (i, μ) ═ μTuiThen the anomaly score z (t) is defined as:
Figure FDA0003147963340000021
in the formula: i represents SqOr SaThe ith feature vector in the time series; determining the size of a threshold value by corresponding numerical values in the abnormal score sequence according to the occurrence condition of the abnormal precursors of the magnetic storm space in the sequence to be monitored;
after the threshold is determined, selecting a time t, and extracting the current TEC time sequence by using a sliding window with the length of w:
s(t)＝(si,si+1,...si+w-1)Tthe historical TEC time sequence is defined as S1The TEC time sequence to be monitored is S2：
Figure FDA0003147963340000022
In the formula, gamma is a positive integer, the time lags behind historical data, the steps (i) - (c) are repeated, the calculated z (t) sequence is compared with a threshold value, and if the score value in z (t) is larger than a given threshold value, the occurrence and the occurrence time of the abnormal event are judged.
2. The method for monitoring the real-time anomaly of the single-station ionized layer TEC according to claim 1, wherein in the third step, singular value decomposition is used for resolving the eigenvalue by a Lanczos-QR combination method, and a tri-diagonal matrix T is obtained by successively tri-diagonalizing a symmetric matrix A, so that the eigenvalue and eigenvector of the tri-diagonal matrix T are approximate to the eigenvalue and eigenvector of the symmetric matrix A, and the expression form of the tri-diagonal matrix T is as follows:
Figure FDA0003147963340000023
adopting Lanczos to iteratively solve alpha and beta variables in the formula (8), firstly, utilizing a power method to solve the abnormal TEC time sequence S to be monitoredaFirst normalized feature vector q1＝μ，
Figure FDA0003147963340000024
β0＝1，q01, calculate:
Figure FDA0003147963340000031
then i +1, repeating the formula (9) until i ═ ns, and sequentially calculating α1，β1，α2，β2，…，αns；
After the tri-diagonal matrix T is determined, a QR algorithm is adopted for characteristic decomposition, namely T is PXPTWhere P is the orthogonal eigenvector, X is the eigenvalue, P is initialized to the identity matrix, i is 1, T1T, calculate:
Figure FDA0003147963340000032
in the formula, R is a nonsingular upper triangular matrix;
and then i +1, repeating the formula (10) until the difference T-P is smaller than the threshold value 1e-3, finishing the iteration, taking the first m components of the feature vector, and calculating the abnormal score according to the formula (6).
CN202110758077.5A 2021-07-05 2021-07-05 Real-time anomaly monitoring method suitable for single-station ionized layer TEC Withdrawn CN113625356A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114384556A (en) * 2021-12-31 2022-04-22 中国电波传播研究所（中国电子科技集团公司第二十二研究所） Regional high-resolution ionosphere TEC map reconstruction method
CN116400385A (en) * 2023-03-21 2023-07-07 湖北珞珈实验室 System and method for detecting coupling abnormality of bottom atmosphere and ionized layer

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114384556A (en) * 2021-12-31 2022-04-22 中国电波传播研究所（中国电子科技集团公司第二十二研究所） Regional high-resolution ionosphere TEC map reconstruction method
CN114384556B (en) * 2021-12-31 2024-05-14 中国电波传播研究所（中国电子科技集团公司第二十二研究所） Regional high-resolution ionosphere TEC map reconstruction method
CN116400385A (en) * 2023-03-21 2023-07-07 湖北珞珈实验室 System and method for detecting coupling abnormality of bottom atmosphere and ionized layer
CN116400385B (en) * 2023-03-21 2024-01-12 湖北珞珈实验室 System and method for detecting coupling abnormality of bottom atmosphere and ionized layer

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