Parameter interpretation method and system of ionospheric oblique backscatter sweep-frequency ionogram

Abstract

The application discloses an ionospheric oblique backscatter scattering sweep ionogram parameter interpretation method and system, including the following steps: carrying out gray scale processing on the oblique backscatter scattering sweep ionogram; carrying out noise removal on the ionogram; extracting the image edge through a Canny edge detection operator; connecting the frequency point signals submerged by noise through morphological closing operation to obtain continuous signal edges; dividing the ionospheric vertical sounding signals and the oblique backscatter scattering signals through connected domain analysis; demarcating the starting area of the F layer oblique backscatter scattering signals, and traversing the ionogram along the frequency direction to obtain the frequency-minimum group path curve of the oblique backscatter scattering signals; and using LASSO regression to fit the frequency-minimum group path curves of different frequency points to obtain the F layer oblique backscatter scattering front edge; the application has the advantages of accurate interpretation, small noise interference and good signal quality.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of ionospheric inversion, and particularly relates to a parameter interpretation method and system of an ionospheric oblique backscatter scatter sweep ionogram. BACKGROUND

[0002] The principle of ionospheric oblique backscatter detection is that radio waves are transmitted by a transmitter, obliquely incident on the ionosphere, and then reflected to the far end of the earth ground or water surface. Due to the unevenness of the incident plane, part of the rays return along the original path and are received by the receiver. The detection equipment obtains the group delay information of the oblique backscatter echo at different frequencies through sweep detection, and obtains a frequency-group delay two-dimensional ionogram. Due to spherical focusing and time focusing, the oblique backscatter echo has a very steep front, and presents obvious signal enhancement on the ionogram, and its group path is the smallest among all oblique backscatter echoes at the detection frequency.

[0003] Due to at least two ionospheric reflections and one ground or water surface scattering, the energy of the oblique backscatter signal is weak. In addition, as the detection frequency increases, the oblique backscatter signal front group path monotonically increases, which will bring greater path loss. Therefore, the signal-to-noise ratio of the oblique backscatter signal in the oblique backscatter ionogram is usually low, and due to the existence of a large number of interference in the same frequency band in space, the echo at many frequencies is submerged in the interference, and the front of the oblique backscatter ionogram is broken. Due to the large detection power of the oblique backscatter, the sidelobes of the transmitting antenna can still radiate a large power signal, and part of the signal is vertically incident on the ionosphere and is vertically reflected and received by the receiver. Since the vertical measurement signal of the vertical reflection has no scattering process, the energy of the vertical measurement signal received by the receiver is even higher than that of the oblique backscatter signal. The existence of the above phenomenon makes it very difficult to automatically interpret the complete oblique backscatter signal front.

[0004] At present, the automatic parameter interpretation method of the oblique backscatter ionogram mainly depends on traversing the frequency axis and extracting the minimum group path of each frequency point as the front by threshold method, but this method has high requirements for the oblique backscatter ionogram, and is difficult to use under the condition of small power detection, and only a part of the broken oblique backscatter front can be obtained; at the same time, the interpretation result is greatly affected by the vertical measurement signal. SUMMARY

[0005] The application aims to provide a method and system for parameter interpretation of ionospheric oblique backscatter sweep-frequency ionograms, which uses the signal enhancement of the front of the oblique backscatter signal in the ionospheric oblique backscatter ionogram caused by the focusing effect, repairs the image submerged by interference through two-dimensional discrete wavelet reconstruction, extracts the edge through the Canny edge detection operator, separates the oblique backscatter signal and the vertical reflection signal through the morphological closing operation and the prior knowledge of the ionospheric signal group path distribution, realizes the parameter interpretation, and has the advantages of accurate interpretation, small noise interference and good signal quality.

[0006] To achieve the above-mentioned purpose, the technical scheme adopted by the application is as follows: a method for parameter interpretation of ionospheric oblique backscatter sweep-frequency ionograms, comprising the following steps:

[0007] Performing gray processing and denoising on the oblique backscatter sweep-frequency ionogram;

[0008] After the image edge is extracted by the Canny edge detection operator, the frequency point signals submerged by noise are connected through the morphological closing operation to obtain the continuous signal edge;

[0009] Dividing the ionospheric vertical measurement signal and the oblique backscatter signal through connected domain analysis;

[0010] Defining the starting area of the F layer oblique backscatter signal, and traversing the ionogram along the frequency direction to obtain the frequency-minimum group path curve of the oblique backscatter signal; and using LASSO regression to fit the frequency-minimum group path curves of different frequency points to obtain the F layer oblique backscatter front.

[0011] In the above-mentioned scheme, the oblique backscatter sweep-frequency ionogram is first subjected to gray processing and denoising to improve the edge extraction effect, the frequency point signals submerged by noise are connected through the morphological closing operation after the image edge is extracted by the Canny edge detection operator to pre-process and repair the denoised image, the continuous signal edge is obtained, the ionospheric vertical measurement signal and the oblique backscatter signal are divided through connected domain analysis for easy identification, and after the ionospheric oblique backscatter signal group path area is determined, the ionospheric oblique backscatter front is obtained by using LASSO regression to fit the minimum values of the ionospheric oblique backscatter signal group path of different frequency points.

[0012] Further, the denoising comprises two-dimensional discrete wavelet decomposition of the gray-processed oblique backscatter sweep-frequency ionogram, and two-dimensional wavelet reconstruction to obtain the denoised oblique backscatter ionogram.

[0013] Further, the Canny edge detection operator extracting the image edge comprises the following steps:

[0014] A Gaussian filter mask is defined, the mask is used to scan each pixel of the image, and the weighted average gray value of the pixels in the neighborhood determined by the mask is used to replace the value of the center pixel of the template. The image pixel gradient is calculated using the Sobel operator, and then two threshold values are set and the edge pixels are traversed to obtain a binary image containing the edges of the oblique backscatter signal and the vertical sounding signal.

[0015] Further, the morphological closing operation on the frequency point signal submerged by noise comprises the following steps:

[0016] A rectangular kernel is constructed The binary image containing the edges of the oblique backscatter signal and the vertical sounding signal is subjected to a morphological closing operation and the kernel The morphological closing operation is represented as:

[0017] 。

[0018] Further, the division of the ionospheric vertical sounding signal and the oblique backscatter signal through connected domain analysis comprises the following steps:

[0019] After the connected domain analysis, a threshold value is selected to remove the connected domains with too few connected pixels, and the ionospheric background mask, the vertical sounding signal mask, and the oblique backscatter signal mask are obtained by traversing each connected domain and classifying.

[0020] Further, the following steps are included: individually interpreting the ionospheric vertical sounding signal to obtain the local ionospheric critical frequency and the electron density peak height, and the interpretation steps are as follows:

[0021] The pixel position of the ionospheric oblique backscatter signal mask is set to 0, and it is determined whether the vertical sounding signal mask exists. If it exists, the maximum frequency corresponding to the vertical sounding signal mask is determined as the ionospheric vertical sounding x wave critical frequency. The ionospheric vertical sounding x wave critical frequency and the magnetic spin frequency in the space above the location of the receiving device of the ionospheric map are used to calculate the ionospheric vertical sounding o wave critical frequency and the ionospheric peak height.

[0022] If the vertical sounding signal mask does not exist, the ionospheric critical frequency and the peak height parameters are provided by the model.

[0023] Further, the following steps are included:

[0024] The vertical sounding signal mask is interpreted, the minimum group path of the minimum frequency point is extracted, and the minimum group path of the F layer oblique backscatter signal region is calculated. ​

[0025] Along the ionization chart frequency axis direction traverses, to the F layer oblique return scattering signal area in the oblique return scattering signal mask corresponding position pixel value less than the set value of the pixel is zero, and selects each frequency point The minimum group path of the non-zero pixel point, the frequency-minimum group path curve of the oblique return scattering signal is obtained.

[0026] Further, the F layer oblique return scattering front is a cubic polynomial function fitted by the frequency point number of the oblique return scattering signal, the frequency-minimum group path curve, the oblique return scattering signal frequency matrix, the regularization parameter, the polynomial coefficient matrix, and the norm of the polynomial coefficient matrix.

[0027] An ionospheric oblique return scattering sweep frequency ionogram parameter interpretation system, comprising:

[0028] The first main module is used for gray processing and denoising of the oblique return scattering sweep frequency ionogram.

[0029] The second main module is used for extracting the image edge through the Canny edge detection operator, and then performing morphological closing operation on the frequency point signal submerged by noise to obtain a continuous signal edge.

[0030] The third main module is used for dividing the ionospheric vertical signal and the oblique return scattering signal through connected domain analysis.

[0031] The fourth main module is used for demarcating the F layer oblique return scattering signal starting area, and traversing the ionogram along the frequency direction to obtain the frequency-minimum group path curve of the oblique return scattering signal; and using LASSO regression to fit the frequency-minimum group path curve of different frequency points to obtain the F layer oblique return scattering front.

[0032] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to make the computer execute the steps of the method.

[0033] Compared with the prior art, the beneficial effects of the present application are:

[0034] 1. The present application uses morphological closing operation to effectively improve the quality of the oblique return scattering signal of the ionogram, and can repair the broken oblique return scattering front under the condition of severe interference.

[0035] 2. The present application effectively separates the oblique return scattering signal and the vertical signal of the ionogram, additionally obtains the local ionospheric parameter, further reduces the interference of the vertical signal, and improves the accuracy of the oblique return scattering signal front.

[0036] ​3、The present application utilizes LASSO regression to fit the oblique backscattering front, effectively weakens the influence of noise on the front position, and the introduction of the regularization term reduces the risk of overfitting; in combination with two-dimensional discrete wavelet decomposition and two-dimensional wavelet reconstruction, the denoising effect is improved;

[0037] 4、The present application provides an image algorithm scheme, which does not need to change the hardware configuration, is easy to implement, and has good compatibility. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 A principle schematic diagram of a parameter interpretation method of an ionospheric oblique backscattering sweep-frequency ionogram provided by the embodiment of the present application;

[0039] Figure 2 A step diagram of a parameter interpretation method of an ionospheric oblique backscattering sweep-frequency ionogram provided by the embodiment of the present application;

[0040] Figure 3 An example diagram of Canny edge extraction on an ionogram provided by the embodiment of the present application;

[0041] Figure 4 An example diagram of morphological closing operation on an edge binary image provided by the embodiment of the present application;

[0042] Figure 5 An example diagram of connected domain analysis marking vertical reflection signals and oblique backscattering signals provided by the embodiment of the present application;

[0043] Figure 6 An example diagram of vertical reflection signal extraction provided by the embodiment of the present application;

[0044] Figure 7 An example diagram of oblique backscattering front extraction provided by the embodiment of the present application; DETAILED DESCRIPTION

[0045] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application. In addition, the technical features in each embodiment or in a single embodiment provided by the present application can be combined with each other to form new technical schemes, and the combination is not restricted by the order of steps and / or structure composition mode, but should be based on the fact that it can be realized by those skilled in the art. When the combination of technical schemes appears contradictory or unfeasible, it should be considered that the combination of technical schemes does not exist, and is not within the protection scope required by the present application.

[0046] The F layer is the ionospheric region, typically ranging from 140 kilometers to several thousand kilometers in altitude. During the day, the F layer is divided into... Layers and layer, The layer is located Below this layer, the altitude is approximately 140 to 200 kilometers.

[0047] This embodiment provides a method for interpreting parameters of an ionospheric oblique return scattering swept frequency ionograph. Utilizing conventional oblique return scattering detection equipment, it helps improve the quality of the oblique return scattering ionograph without increasing additional hardware costs. It can effectively separate the vertical reflection signal and the oblique return scattering signal on the ionograph, and additionally obtain local ionospheric parameters and a more continuous oblique return scattering front.

[0048] The basic principle of this embodiment is as follows: Figure 1 As shown, a shortwave probe signal with a narrow bandwidth is emitted into the ionosphere obliquely upwards using an ionospheric oblique backscattering detector in a frequency-sweeping observation mode. After being received by the receiver, the signal is cross-correlated with the code sequence and pulse compression is performed to obtain a two-dimensional ionosphere distribution map of frequency-group paths. After constant false alarm rate (CFAR) testing, the oblique backscattering frequency-sweeping ionosphere map is used for grayscale processing and wavelet denoising. After extracting the image edges using the Canny edge detection operator, the frequency points submerged by noise are connected by morphological closing operations. The ionospheric vertical sounding signal and the oblique backscattering signal are separated by the centroid of the connected domain. The local ionospheric critical frequency and electron density peak height are obtained by separately interpreting the ionospheric vertical sounding signal, thereby determining the ionospheric oblique backscattering signal group path region. The minimum value of the ionospheric oblique backscattering signal group path at different frequency points is fitted using LASSO regression to obtain the ionospheric oblique backscattering front.

[0049] like Figure 2 As shown, the implementation of this invention includes the following steps:

[0050] Step 1: Obtain the oblique return scattering sweep frequency ionization map after constant false alarm rate processing, and convert it into grayscale image format;

[0051] It should be noted that the false alarm rate for constant false alarm rate (CFAR) analysis of oblique return scattering sweep ionization maps needs to be less than [a certain value]. ;

[0052] Step 2: Perform two-dimensional discrete wavelet decomposition on the oblique return scattering swept frequency ionization map obtained in step 1 of grayscale processing, retain the approximation coefficients, perform soft threshold quantization on the detail coefficients, and then perform two-dimensional wavelet reconstruction to obtain the denoised oblique return scattering ionization map.

[0053] It should be noted that if the x-axis scale of the oblique return scattering sweep ionization map pixels is... The y-axis scale is The pixel value function is The two-dimensional scaling function is The two-dimensional wavelet function transformed along the horizontal edge direction is: The two-dimensional wavelet function transformed along the vertical edge direction is: The two-dimensional wavelet function transformed along the diagonal edge direction is: The number of decomposition layers is Then the two-dimensional discrete scale and translation basis functions are expressed as equation (1) and equation (2), respectively:

[0054] Equation (1)

[0055] Equation (2)

[0056] If the number of pixels on the x-axis of the oblique return scattering sweep ionization map is... The number of pixels along the y-axis is , where m represents the horizontal offset of the wavelet basis function and n represents the vertical offset of the wavelet basis function. The pixel value function is... The low-frequency subband and high-frequency subband of the discrete wavelet transform are expressed as equation (3) and equation (4), respectively:

[0057] Equation (3)

[0058] Equation (4)

[0059] in, These represent horizontal, vertical, and diagonal directions, respectively.

[0060] The low-frequency subband is fully preserved, and soft-threshold denoising is applied to the high-frequency subband separately. If the matrix row sorting function is expressed as... Then the matrix with high-frequency subband rows sorted in ascending order. Represented as:

[0061] Equation (5)

[0062] For dimensions. The retained scale value is expressed as... The value range is generally 100%. Threshold function Represented as:

[0063] Equation (6)

[0064] High-frequency subband soft threshold denoising coefficients Represented as:

[0065] Equation (7)

[0066] It is a symbolic function.

[0067] Two-dimensional wavelet reconstruction of oblique return scattering ionization map function Represented as:

[0068] Equation (8)

[0069] Step 3: Use the Canny edge detection operator to perform edge detection on the oblique return ionization map obtained in Step 2, and obtain a binary image containing the edges of the oblique return ionization signal and the vertical signal.

[0070] It should be noted that the edge detection method is as follows:

[0071] Define a 5x5 Gaussian filter mask, whose distribution is represented as:

[0072] Equation (9)

[0073] The mask is used to scan each pixel of the image, and the weighted average gray value of the pixels in the neighborhood determined by the mask is used to replace the value of the center pixel of the template, as shown below:

[0074] Equation (10)

[0075] The Sobel operator is used to calculate the pixel gradient of the image. The gradients along the x-axis and y-axis are expressed as equations (11) and (12), respectively:

[0076] Equation (11)

[0077] Equation (12)

[0078] The magnitude and direction of the image pixel gradient are expressed by equations (13) and (14), respectively:

[0079] Equation (13)

[0080] Equation (14)

[0081] Image gradient directions are categorized into eight directions: horizontal (left, right), vertical (top, bottom), and diagonal (top right, bottom right, top left, bottom left). The algorithm iterates through all pixels in the image. If a pixel's gradient is a local maximum in either a positive or negative gradient direction, that pixel is retained and defined as an edge pixel; otherwise, its pixel value is set to zero. Two thresholds are set. and and satisfy Then iterate through the edge pixels again. If the gradient magnitude of the edge pixels... Greater than or equal to If the gradient magnitude of the edge pixel is high, then the edge pixel is marked as a strong edge pixel. Less than But greater than or equal to If the gradient magnitude of the edge pixel is... Less than If the edge point is discarded and its pixel value is set to zero, a binary image containing the edges of the oblique return scattering signal and the vertical projection signal is obtained; typically, and The values ​​are respectively and .

[0082] Step 4, using morphological closing operations to fill in the frequency points where the signal is overwhelmed by interference to obtain continuous signal edges, is as follows:

[0083] Constructing a rectangular kernel is represented as:

[0084] Equation (15)

[0085] The binary image obtained in step 3 containing the edges of the oblique return scattering signal and the vertical projection signal. With nuclear The morphological closing operation is represented as:

[0086] Equation (16)

[0087] Step 5: Perform connected component analysis on the signal edges obtained in Step 4, select a threshold and remove connected components with too few connected pixels, and divide the ionospheric vertical scattering signal, oblique return scattering signal and background noise by the centroid height of the remaining connected components.

[0088] It should be noted that the method for removing noisy connected components and separating ionospheric vertical scattering signals, oblique return scattering signals, and background noise using connected component analysis is as follows:

[0089] If the signal edge The set of connected component masks obtained by connected component analysis is The background connected component mask is , No. A mask for a signal or noise connected region is , Let be the summation function, and let the centroid of the connected region be... , This refers to the group path corresponding to the centroid of a connected component. This refers to the frequency corresponding to the centroid of a connected component; the total number of connected components is... The ionization map background mask, vertical signal mask, and oblique return scattering signal mask obtained by traversing each connected domain and classifying into three categories are expressed as Equations (17), (18), and (19), respectively:

[0090] Equation (17)

[0091] Equation (18)

[0092] Equation (19)

[0093] In some embodiments, step 6 includes interpreting the ionospheric vertical measurement signal obtained in step 5 to obtain the local ionospheric critical frequency and electron density peak height.

[0094] It should be noted that the method for interpreting ionospheric vertical measurement signals to obtain the local ionospheric critical frequency and electron density peak height is as follows:

[0095] Set the pixel at the position of the oblique return scattering signal mask on the ionosphere to 0, determine whether a vertical sensing signal mask exists, and if so, determine the maximum frequency corresponding to the vertical sensing signal mask as the ionospheric vertical sensing signal X-wave. Layer critical frequency If the vortex frequency above the location of the receiving device for acquiring the ionization map is Then the vertical measurement signal o-wave of the ionosphere The critical frequency of the layer is expressed as:

[0096] Equation (20)

[0097] ionosphere The peak height of the layer is expressed as:

[0098] Equation (21)

[0099] If the vertical measurement signal mask is not present, then the ionosphere... The layer critical frequency and peak height parameters are derived from Model provided;

[0100] Step 6: Using the group path of the minimum frequency point of the ionospheric vertical measurement signal obtained in Step 5, delineate the starting region of the oblique return scattering signal of the F layer, and traverse the ionization map along the frequency direction to obtain the frequency-minimum group path curve of the oblique return scattering signal.

[0101] It should be noted that the method for defining the starting region of the oblique return scattering signal and obtaining the frequency-minimum group path curve of the oblique return scattering signal is as follows:

[0102] Interpret the ionospheric vertical measurement signal mask obtained in step 5 and extract the group path of the minimum frequency point. Then the minimum group path in the region of obliquely returned scattered signals in layer F satisfies:

[0103] Equation (22)

[0104] Traverse along the frequency axis of the ionization diagram, set the pixel values ​​of the corresponding positions of the oblique return scattering signal mask in the F layer oblique return scattering signal region to zero, and select the minimum group path of the non-zero pixel points at each frequency point to obtain the frequency-minimum group path curve of the oblique return scattering signal.

[0105] Step 6: Perform LASSO regression fitting on the frequency-minimum group path curve of the obtained oblique return scattering signal to serve as the leading edge of the oblique return scattering signal in layer F.

[0106] It should be noted that the method for fitting the frequency-minimum group path curve of the oblique return scattering signal to LASSO regression, and using this curve as the leading edge of the oblique return scattering signal, is as follows:

[0107] The leading edge of the obliquely returned scattered signal is fitted by a cubic polynomial function of frequency, expressed as:

[0108] Equation (23)

[0109] Wherein, the polynomial coefficient matrix This is obtained by minimizing the cost function, and is expressed as:

[0110] Equation (24)

[0111] in, To include the number of frequency points of the oblique return scattering signal, The frequency-minimum group path curve obtained in step 6, The frequency matrix of the obliquely returned scattered signal. This is the regularization parameter, typically set to a value of [value to be filled in]. , The coefficient matrix of the polynomial of Norm.

[0112] In the above method, the edge detection effect can be adjusted by adjusting the size of the dual threshold of the Canny edge detection operator as needed. By increasing the size of the regularization parameter of the LASSO regression fitting, the obtained oblique return scattering front can be made smoother, but if it is too large, the fitted front position will deviate from the real front position.

[0113] In specific implementation, such as Figure 3As shown, in one embodiment, this application performs Canny edge extraction on the oblique return scattering ionization map. Due to the implementation of the hysteresis threshold extraction algorithm, a large amount of discrete noise is suppressed, and the high-frequency echoes can be well preserved even if the energy is weak.

[0114] like Figure 4 As shown, this is an example of performing morphological closing operations on the edge binarized image in this embodiment. A large number of isolated edges are connected, and noise is further suppressed.

[0115] like Figure 5 As shown, this is an example diagram of vertical reflection signals and oblique return scattering signals after connected component analysis in this embodiment. The green part is the oblique return scattering signal mask, and the red part is the vertical reflection signal mask. The classification is accurate, and more isolated noise is suppressed.

[0116] like Figure 6 The diagram shown is an example of vertical reflection signal extraction in this embodiment. A mask is used to define the vertical reflection signal region, and the pixel values ​​in other regions are set to zero. This yields an ionization map containing only the vertical reflection signal. The maximum frequency corresponding to each pixel is then used to obtain the signal. By combining the local magnetic gyro frequency, one can obtain Layer critical frequency With peak height ;

[0117] like Figure 7 The diagram shown is an example of oblique return scattering front extraction in this embodiment. The oblique return scattering front is accurately extracted, and even with severe interference and many frequency signals being submerged, the oblique return scattering signal front can still be accurately extrapolated to... The desired effect was achieved in the vicinity;

[0118] A parameter interpretation system for an ionospheric oblique return scattering swept frequency ionization map includes:

[0119] The first main module is used to perform grayscale processing and noise reduction on the oblique return scattering sweep frequency ionization map;

[0120] The second main module is used to extract image edges using the Canny edge detection operator and then connect the frequency point signals that are submerged by noise through morphological closing operations to obtain continuous signal edges.

[0121] The third main module is used to separate the vertical ionospheric signal and the oblique return scattering signal through connected component analysis;

[0122] The fourth main module is used to delineate the starting region of the oblique return scattering signal of the F layer, and to traverse the ionization map along the frequency direction to obtain the frequency-minimum group path curve of the oblique return scattering signal; LASSO regression is used to fit the frequency-minimum group path curve at different frequency points to obtain the oblique return scattering front of the F layer.

[0123] A computer-readable storage medium storing a computer program that, when executed by a processor, causes a computer to perform the steps of the method described above.

[0124] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for interpreting parameters of an ionospheric oblique return scattering swept frequency ionization map, characterized in that, Includes the following steps: Grayscale processing and noise reduction were performed on the oblique return scattering sweep frequency ionization map; After extracting the image edges using the Canny edge detection operator, morphological closing operations are used to connect the frequency point signals that are submerged by noise, resulting in continuous signal edges. The ionospheric vertical scattering signal and oblique return scattering signal are divided by connected component analysis. After connected component analysis, a threshold is selected to remove connected components with too few connected pixels. The background mask, vertical scattering signal mask, and oblique return scattering signal mask of the ionosphere are obtained by traversing each connected component and classifying them. The local ionospheric critical frequency and electron density peak height are obtained by individually interpreting the ionospheric vertical measurement signal. The interpretation steps are as follows: Set the pixels at the oblique return scattering signal mask positions on the ionosphere image to 0, determine if a vertical sensing signal mask exists, and if so, determine the maximum frequency corresponding to the vertical sensing signal mask as the critical frequency of the F2 layer of the ionospheric vertical sensing signal x-wave. Then, based on the critical frequency of the F2 layer of the ionospheric vertical sensing signal x-wave and the magnetotropic frequency f above the location of the receiving equipment on the ionosphere image... B The critical frequency of the ionospheric vertical measurement signal o-wave in the F2 layer and the peak height of the ionospheric F2 layer were calculated. If the vertical measurement signal mask does not exist, the critical frequency and peak height parameters of the F2 ionospheric layer are provided by the IRI model; The region where the oblique return scattering signal of the F layer begins was delineated, and the ionization map was traversed along the frequency direction to obtain the frequency-minimum group path curve of the oblique return scattering signal. The oblique return scattering front of the F layer was obtained by fitting the frequency-minimum group path curve at different frequency points using LASSO regression.

2. The parameter interpretation method for the ionospheric oblique return scattering swept frequency ionization map as described in claim 1, characterized in that: The denoising process involves performing two-dimensional discrete wavelet decomposition on the grayscale-processed oblique return scattering sweep frequency ionization map, followed by two-dimensional wavelet reconstruction to obtain the denoised oblique return scattering ionization map.

3. The parameter interpretation method for the ionospheric oblique return scattering swept frequency ionization map as described in claim 1, characterized in that: The Canny edge detection operator extracts image edges through the following steps: Define a Gaussian filter mask, use the mask to scan each pixel of the image, and replace the value of the center pixel of the template with the weighted average gray value of the pixels in the neighborhood determined by the mask. Calculate the pixel gradient of the image using the Sobel operator, and then obtain a binary image containing the edges of the oblique return scattering signal and the vertical scattering signal by setting two thresholds and traversing the edge pixels.

4. The parameter interpretation method for the ionospheric oblique return scattering swept frequency ionization map as described in claim 3, characterized in that: The morphological closing operation to connect the frequency point signal submerged by noise includes the following steps: Construct a rectangular kernel B for the binary image containing the edges of the oblique return scattering signal and the vertical projection signal. The morphological closing operation with kernel B is represented as: 。 5. The parameter interpretation method for the ionospheric oblique return scattering swept frequency ionization map as described in claim 1, characterized in that: It includes the following steps: Interpret the vertically oriented signal mask, extract the group path of the minimum frequency point, and then calculate the minimum group path of the oblique return scattering signal region of layer F; Traverse along the frequency axis of the ionization diagram, set the pixel values ​​of the corresponding positions of the oblique return scattering signal mask in the F layer oblique return scattering signal region to zero, and select the minimum group path of the non-zero pixel points at each frequency point to obtain the frequency-minimum group path curve of the oblique return scattering signal.

6. The parameter interpretation method for the ionospheric oblique return scattering swept frequency ionization map as described in claim 1, characterized in that: The F-layer oblique return scattering front is defined by the number of frequency points of the oblique return scattering signal, the frequency-minimum group path curve, the frequency matrix of the oblique return scattering signal, the regularization parameter, and the polynomial coefficient matrix. l 1 A cubic polynomial function fitted by norm.

7. A parameter interpretation system for an ionospheric oblique return scattering swept frequency ionization map, applied to the parameter interpretation method for an ionospheric oblique return scattering swept frequency ionization map as described in any one of claims 1-6, characterized in that, include: The first main module is used to perform grayscale processing and noise reduction on the oblique return scattering sweep frequency ionization map; The second main module is used to extract image edges using the Canny edge detection operator and then connect the frequency point signals that are submerged by noise through morphological closing operations to obtain continuous signal edges. The third main module is used to separate the vertical ionospheric signal and the oblique return scattering signal through connected component analysis; The fourth main module is used to delineate the starting region of the oblique return scattering signal of the F layer, and to traverse the ionization map along the frequency direction to obtain the frequency-minimum group path curve of the oblique return scattering signal; LASSO regression is used to fit the frequency-minimum group path curve at different frequency points to obtain the oblique return scattering front of the F layer.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it causes the computer to perform the steps of the parameter interpretation method for the ionospheric oblique backscattering swept frequency ionization map as described in any one of claims 1 to 6.

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