Low ionosphere remote sensing detection method

Abstract

The invention discloses a low ionosphere remote sensing detection method, and belongs to the technical field of very low frequency fluctuation detection and application, and the method comprises the steps: obtaining a plurality of lightning very low frequency signals; for the ith lightning very-low-frequency signal, calculating a plurality of analog very-low-frequency signals corresponding to different reflection heights and different sharpness; determining the equivalent average electron density of the ith lightning very-low-frequency signal based on the plurality of analog very-low-frequency signals corresponding to the ith lightning very-low-frequency signal; establishing a tomography equation based on each lightning very-low frequency signal and the equivalent average electron density of each lightning very-low frequency signal; the tomography equation is solved, an electron density tomography result of a target area is obtained, and the target area is a set area in the wide-area low ionized layer. According to the method, wide-area low-ionosphere remote sensing detection can be accurately carried out.

Description

Technical Field

[0001] The present disclosure relates to the technical field of very low frequency (VLF) wave detection and applications, and particularly to a method for remote sensing detection of the lower ionosphere. Background Art

[0002] The lower ionosphere, namely the D layer of the ionosphere, is generally the partially ionized atmosphere at a distance of 60 - 100 kilometers from the ground, which is affected by multiple factors such as solar activity, geomagnetic activity, and atmospheric gravity waves. Clarifying the electron density variation in the lower ionosphere is of great significance for application fields such as space weather effects, long-wave communication, and navigation systems.

[0003] In related technologies, the method for remote sensing detection of the lower ionosphere includes the ionosphere detection method based on VLF (Very Low Frequency) signals. During the propagation of VLF signals, the change in the ionospheric electron density will cause phenomena such as signal attenuation, phase change, and frequency drift. These changes can provide important information about the electron density in the lower ionosphere. Based on this characteristic, through the real-time observation and analysis of VLF signals, the distribution of the electron density in the lower ionosphere can be effectively inferred.

[0004] However, the ionosphere detection method based on VLF signals has certain limitations: the traditional VLF signal detection technology relies on fixed transmitting stations, and there are problems of limited coverage and limited detection accuracy. Summary of the Invention

[0005] The present disclosure provides a method for remote sensing detection of the lower ionosphere, which can accurately perform wide-area remote sensing detection of the lower ionosphere. The technical solution at least includes the following: In a first aspect, a method for remote sensing detection of the lower ionosphere is provided, including: obtaining a plurality of lightning very low frequency signals; for the i-th lightning very low frequency signal, calculating a plurality of simulated very low frequency signals corresponding to different reflection heights and different sharpnesses; based on the plurality of simulated very low frequency signals corresponding to the i-th lightning very low frequency signal, determining the equivalent average electron density of the i-th lightning very low frequency signal; based on each lightning very low frequency signal and the equivalent average electron density of each lightning very low frequency signal, establishing a tomography equation; solving the tomography equation to obtain the electron density tomography result of the target area, where the target area is a set area in the wide-area lower ionosphere; where i is a positive integer, and the value range of i is from 1 to I, and I is the total number of lightning very low frequency signals.

[0006] Optionally, the tomography equation is represented by the following formula:

[0007] Where denotes the propagation path integral matrix of the \(i\)-th lightning very low frequency signal, denotes the equivalent average electron density of the \(i\)-th lightning very low frequency signal, is the quantity to be solved, representing the discrete cosine transform coefficient vector of the low ionospheric electron density distribution.

[0008] Optionally, in the tomography equation, the propagation path of each lightning very low frequency signal is evenly divided into \(R\) segments, the target area is divided into \(M\times N\) longitude and latitude grids, and the element in the \(p\)-th row and \(q\)-th column of the propagation path integral matrix of the \(i\)-th lightning very low frequency signal is expressed by the following formula:

[0009] where, is the great circle path length from the lightning to the detection station in the \(i\)-th lightning very low frequency signal, is the length of the \(r\)-th propagation path of the \(i\)-th lightning very low frequency signal, \(r\) is an integer, and the value range of \(r\) is from 1 to \(R\), is the central latitude of the \(r\)-th propagation path of the \(i\)-th lightning very low frequency signal, is the central longitude of the \(r\)-th propagation path of the \(i\)-th lightning very low frequency signal, is a parameter in the inverse discrete cosine transform.

[0010] Optionally, for the \(i\)-th lightning very low frequency signal, calculating multiple simulated very low frequency signals corresponding to different reflection heights and different sharpnesses includes: at the first reflection height and the first sharpness, calculating the simulated very low frequency signal of the \(i\)-th lightning very low frequency signal in the following manner: evenly dividing the lightning discharge path from the cloud to the ground corresponding to the \(i\)-th lightning very low frequency signal into \(N\) segments; based on the lightning current model, calculating the lightning current of each segment of the \(N\) segments of the discharge path of the \(i\)-th lightning very low frequency signal to obtain \(N\) lightning currents; performing Fourier transform on the distribution of the \(N\) lightning currents to obtain \(N\) lightning current frequency components; based on the first reflection height and the first sharpness, using the long wave propagation LWPC model to calculate the simulated very low frequency signal components corresponding to each lightning current frequency component to obtain \(N\) simulated very low frequency signal components; adding the \(N\) simulated very low frequency signal components to obtain the simulated very low frequency signal of the \(i\)-th lightning very low frequency signal at the first reflection height and the first sharpness.

[0011] Optionally, determining the equivalent average electron density of the \(i\)-th lightning very low frequency (VLF) signal based on multiple analog VLF signals corresponding to the \(i\)-th lightning VLF signal includes: calculating the standard deviation between each lightning analog signal and the \(i\)-th lightning VLF signal among the multiple lightning analog signals corresponding to the \(i\)-th lightning VLF signal; calculating the equivalent average electron density of the \(i\)-th lightning VLF signal based on the reflection height and sharpness corresponding to the lightning analog signal with the minimum standard deviation.

[0012] Optionally, the equivalent average electron density of the \(i\)-th lightning VLF signal is calculated using the following formula:

[0013] where is the equivalent average electron density, is the reflection height, is the sharpness, is the height of the lightning VLF signal from the ground.

[0014] In a second aspect, a low ionosphere remote sensing detection device is also provided, including: an acquisition module for acquiring multiple lightning VLF signals; an analog VLF signal calculation module for calculating multiple analog VLF signals corresponding to different reflection heights and different sharpnesses for the \(i\)-th lightning VLF signal; an equivalent average electron density determination module for determining the equivalent average electron density of the \(i\)-th lightning VLF signal based on the multiple analog VLF signals corresponding to the \(i\)-th lightning VLF signal; a tomographic equation establishment module for establishing a tomographic equation based on each lightning VLF signal and the equivalent average electron density of each lightning VLF signal; a solution module for solving the tomographic equation to obtain an electron density tomographic result of a target area, where the target area is a set area in the wide-area low ionosphere; where \(i\) is a positive integer, and the value range of \(i\) is from 1 to \(I\), and \(I\) is the total number of lightning VLF signals.

[0015] Optionally, in the tomographic equation establishment module, the tomographic equation is represented by the following formula:

[0016] where represents the propagation path integral matrix of the \(i\)-th lightning VLF signal, represents the equivalent average electron density of the \(i\)-th lightning VLF signal, is the quantity to be solved, representing the discrete cosine transform coefficient vector of the low ionosphere electron density distribution.

[0017] Optionally, in the tomographic equation establishment module, the propagation path of each of the lightning very low frequency signals is evenly divided into R segments, the target area is divided into M×N longitude and latitude grids, and the element in the p-th row and q-th column of the propagation path integration matrix of the i-th lightning very low frequency signal is represented by the following formula:

[0018] Wherein, the great circle path length from the lightning to the detection station in the i-th lightning very low frequency signal, is the length of the r-th propagation path segment of the i-th lightning very low frequency signal, r is an integer, and the value range of r is from 1 to R, is the central latitude of the r-th propagation path segment of the i-th lightning very low frequency signal, is the central longitude of the r-th propagation path segment of the i-th lightning very low frequency signal, is a parameter in the inverse discrete cosine transform.

[0019] Optionally, the simulated very low frequency signal calculation module is further configured to, at the first reflection height and the first sharpness, calculate the simulated very low frequency signal of the i-th lightning very low frequency signal in the following manner: evenly divide the discharge path from the cloud to the ground of the lightning corresponding to the i-th lightning very low frequency signal into N segments; based on the lightning current model, calculate the lightning current of each of the N discharge path segments in the i-th lightning very low frequency signal to obtain N lightning currents; perform Fourier transform on the N lightning current distributions to obtain N lightning current frequency components; based on the first reflection height and the first sharpness, calculate the simulated very low frequency signal components corresponding to each of the N lightning current frequency components by using the long wave propagation LWPC model to obtain N simulated very low frequency signal components; add the N simulated very low frequency signal components to obtain the simulated very low frequency signal of the i-th lightning very low frequency signal at the first reflection height and the first sharpness.

[0020] Optionally, the equivalent average electron density determination module is further configured to: calculate the standard deviation between each lightning simulation signal and the i-th lightning very low frequency signal among the multiple lightning simulation signals corresponding to the i-th lightning very low frequency signal; calculate the equivalent average electron density of the i-th lightning very low frequency signal based on the reflection height and sharpness corresponding to the lightning simulation signal with the minimum standard deviation.

[0021] Optionally, the equivalent average electron density determination module is further configured to calculate the equivalent average electron density of the i-th lightning very low frequency signal by using the following formula:

[0022] Wherein, is the equivalent average electron density, is the reflection height, is the sharpness, is the height of the lightning very low frequency signal from the ground.

[0023] In a third aspect, a computer device is further provided, including: a memory and a processor. At least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to execute the low ionosphere remote sensing detection method described in the above embodiments.

[0024] In a fourth aspect, a computer-readable storage medium is further provided. At least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor to execute the low ionosphere remote sensing detection method described in the above embodiments.

[0025] In a fifth aspect, a computer program product is provided, including a computer program / instructions. When the computer program / instructions are executed by a processor, the method described in the first aspect is implemented.

[0026] The beneficial effects brought by the technical solutions provided in the embodiments of the present disclosure at least include: In the embodiments of the present disclosure, the lightning very low frequency signal is innovatively used as the natural signal radiation source of the tomography technology to realize the detection of the electron density distribution in the wide-area low ionosphere. By analyzing the changes in the lightning very low frequency signal generated by lightning, a tomography equation is established, and then the electron density distribution of the low ionosphere in the target area is obtained by solving the tomography equation. Since the lightning very low frequency signal generated by lightning is used for low ionosphere remote sensing detection, the dependence on a fixed transmitter in the traditional VLF detection method is avoided. At the same time, due to the random distribution characteristics of the lightning very low frequency signal, a flexible signal acquisition method is provided for the low ionosphere. In the embodiments of the present disclosure, high-precision low ionosphere electron density detection can be achieved using a limited number of lightning very low frequency signals, which has important application value for the research of the ionosphere space environment. This method has excellent spatial and temporal resolutions, strong anti-interference ability, and low detection cost, which is of great significance for the remote sensing detection and application of the low ionosphere, especially in the fields of space weather monitoring, communication system optimization, etc., and has a wide application prospect. Description of the Drawings

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0028] Figure 1 The flowchart of the low ionosphere remote sensing detection method provided by an exemplary embodiment of the present disclosure is shown; Figure 2 The flowchart of the low ionosphere remote sensing detection method provided by another exemplary embodiment of the present disclosure is shown; Figure 3 The schematic diagram of the low ionosphere remote sensing detection result of the target area obtained by the method in the embodiment of the present disclosure; Figure 4 The schematic diagram of the error of the low ionosphere remote sensing detection result of the target area obtained by the method in the embodiment of the present disclosure; Figure 5 The schematic diagram of the structure of the low ionosphere remote sensing detection device provided by an exemplary embodiment of the present disclosure is shown; Figure 6 The schematic diagram of the structure of the computer device provided by the embodiment of the present disclosure. Detailed implementation manners

[0029] Unless otherwise defined, the technical terms or scientific terms used herein shall have the ordinary meanings understood by those of ordinary skill in the field to which the present disclosure belongs. The terms "first", "second", "third" and similar terms used in the specification and claims of the patent application of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, the terms such as "a" or "one" do not indicate a quantity limitation, but indicate that there is at least one. The terms such as "including" or "comprising" mean that the elements or objects appearing before "including" or "comprising" cover the elements or objects listed after "including" or "comprising" and their equivalents, and do not exclude other elements or objects.

[0030] To make the purpose, technical solutions and advantages of the present disclosure clearer, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.

[0031] Figure 1 The flowchart of the low ionosphere remote sensing detection method provided by an exemplary embodiment of the present disclosure is shown, and this method can be executed by a computer device. Refer to Figure 1 , and this method includes: In step 101, a plurality of lightning very low frequency signals are acquired.

[0032] The lightning very low frequency signal is a very low frequency signal generated with lightning as the radiation source. The propagation path of the lightning very low frequency signal starts from the lightning occurrence position and propagates to the detection station.

[0033] When the location of lightning strikes is unknown, the propagation path of the very low frequency (VLF) lightning signals received by the detection stations is also unknown (only the end detection stations are known, and the starting location of the lightning strikes is unknown). Therefore, lightning location needs to be carried out before obtaining multiple VLF lightning signals.

[0034] Lightning location is achieved through a lightning location network. At least three detection stations are required in the lightning location network to accurately locate a lightning strike. The determined lightning location is the starting point (radiation source) of the VLF lightning signal corresponding to the lightning strike. There are many implementation methods for lightning location through a lightning location network in the related art, which will not be elaborated here.

[0035] After lightning location, the propagation path of each VLF lightning signal received by the detection station can be determined.

[0036] In step 102, for the i-th VLF lightning signal, multiple simulated VLF signals corresponding to different reflection heights and different sharpnesses are calculated.

[0037] Where i is a positive integer, and the value range of i is from 1 to I, and I is the total number of VLF lightning signals. Exemplarily, I is greater than or equal to 500.

[0038] The reflection height and sharpness are the boundary conditions of the waveguide when the VLF lightning signal propagates. By setting different reflection heights and sharpnesses, multiple different simulated VLF signals can be calculated.

[0039] Optionally, at the first reflection height and the first sharpness, the following steps a - e are used to calculate the lightning simulation signal of the i-th VLF lightning signal. Where the first reflection height is any one of the reflection heights in step 102, and the first sharpness is any one of the sharpnesses in step 102.

[0040] Step a, divide the discharge path of the lightning corresponding to the i-th VLF lightning signal from the cloud to the ground into N segments.

[0041] Where N is a positive integer.

[0042] Here, each VLF lightning signal is triggered by a lightning strike. Therefore, the i-th VLF lightning signal corresponds to a lightning strike. The lightning strike has been located through step 101, so the triggering location of the lightning strike is known, and the discharge path of the lightning strike from the cloud to the ground is also known. The discharge path of the lightning strike from the cloud to the ground can be divided into N vertical line segments for subsequent calculations.

[0043] Step b, based on the lightning current model, calculate the lightning current of each discharge path segment in the N discharge path segments of the i-th VLF lightning signal, obtaining N lightning currents.

[0044] When calculating the lightning simulation signal, the low ionosphere between the lightning-detection station path and the ground is considered as a uniform waveguide. The low ionosphere is used as the upper boundary of the waveguide and is described by the first reflection height and the first sharpness.

[0045] Here, the lightning current model is an exponential function of time and height, and the lightning current model is expressed by formulas (1) to (2).

[0046] (1) (2) In formulas (1) and (2), is the height from the ground, represents the distance between the discharge path and the ground at time t is the current of the discharge path when represents the distance between the discharge path and the ground at time t is in the case of. is the rise time coefficient, is the fall time coefficient, is the wavefront velocity, is the attenuation coefficient, is the unit step function, is the current constant.

[0047] According to the distance between each discharge path in the N-section discharge path and the ground and the occurrence time of lightning (which can be obtained by the detection station), the above formula (1) or formula (2) can be substituted to obtain the lightning current corresponding to each discharge path , ,……, 。Among them, 、 …… respectively represent the distance between the first discharge path and the ground, the distance between the second discharge path and the ground... the distance between the Nth discharge path and the ground.

[0048] Step c, perform Fourier transform on the N lightning current distributions to obtain N lightning current frequency components.

[0049] Regarding the implementation method of Fourier transform, there are many in the related technologies, which are omitted here for detailed description.

[0050] Step d, based on the first reflection height and the first sharpness, use the LWPC model to calculate the simulated very low frequency signal components corresponding to each lightning current frequency component to obtain N simulated very low frequency signal components.

[0051] The LWPC (Long-Wave Propagation Capability) model can simulate the propagation characteristics of VLF signals in the Earth-ionosphere waveguide. For example, the full-wave method is used to solve the propagation characteristics of the Earth-ionosphere waveguide in the 3 - 30 kHz frequency band.

[0052] In implementation, the first reflection height, the first sharpness, and any lightning current frequency component can be input into the LWPC model, so as to obtain the simulated very low frequency signal component corresponding to this lightning current frequency component. By inputting N lightning current frequency components (as well as the first reflection height, the first sharpness) into the LWPC model, N simulated very low frequency signal components can be obtained.

[0053] Step e: Add the N simulated very low frequency signal components to obtain the simulated very low frequency signal of the i-th lightning very low frequency signal at the first reflection height and the first sharpness.

[0054] For each of the I lightning very low frequency signals, the above steps a to e can be used for processing, so that the simulated very low frequency signal corresponding to each lightning very low frequency signal can be obtained.

[0055] In step 103, based on the multiple simulated very low frequency signals corresponding to the i-th lightning very low frequency signal, determine the equivalent average electron density of the i-th lightning very low frequency signal.

[0056] Optionally, step 103 includes the following two steps.

[0057] The first step: Calculate the standard deviation between each lightning simulation signal and the i-th lightning very low frequency signal among the multiple lightning simulation signals corresponding to the i-th lightning very low frequency signal.

[0058] Regarding the calculation method of the standard deviation, there are many in the related technologies, which are omitted here for detailed description.

[0059] Among the multiple lightning simulation signals corresponding to the i-th lightning very low frequency signal, some lightning simulation signals may be closer to the i-th lightning very low frequency signal, and some lightning simulation signals may be quite different from the i-th lightning very low frequency signal. By calculating the standard deviation between each lightning simulation signal and the i-th lightning very low frequency signal among the multiple lightning simulation signals corresponding to the i-th lightning very low frequency signal, the similarity degree between each lightning simulation signal corresponding to the i-th lightning very low frequency signal and the i-th lightning very low frequency signal can be measured. The lightning simulation signal with the highest similarity degree (that is, the smallest standard deviation) can be used to calculate the equivalent average electron density of the i-th lightning very low frequency signal.

[0060] The second step: Based on the reflection height and sharpness corresponding to the lightning simulation signal with the smallest standard deviation, calculate the equivalent average electron density of the i-th lightning very low frequency signal.

[0061] Optionally, the equivalent average electron density of the lightning very low frequency (VLF) signal is calculated using formula (3).

[0062] (3) In formula (3), is the equivalent average electron density, is the reflection height, is the sharpness, is the height of the lightning VLF signal from the ground.

[0063] Substitute the reflection height, sharpness corresponding to the lightning simulation signal with the minimum standard deviation, and the height of the i-th lightning VLF signal from the ground into formula (3) to calculate the equivalent average electron density of the i-th lightning VLF signal.

[0064] In step 104, based on each lightning VLF signal and the equivalent average electron density of each lightning VLF signal, a tomographic equation is established.

[0065] In step 105, solve the tomographic equation to obtain the tomographic result of the electron density in the target area.

[0066] The target area is a set area in the wide-area low ionosphere, that is, the area in the low ionosphere that needs to be tomographically scanned.

[0067] In the embodiments of the present disclosure, innovatively, the lightning VLF signal is used as the natural signal radiation source of the tomographic scanning technology to realize the detection of the electron density distribution in the wide-area low ionosphere. By analyzing the changes in the lightning VLF signals generated by lightning, a tomographic equation is established, and then the electron density distribution of the low ionosphere in the target area is obtained by solving the tomographic equation. Since the lightning VLF signals generated by lightning are used for low ionosphere remote sensing detection, the dependence on a fixed transmitter in the traditional VLF detection method is avoided. At the same time, due to the random distribution characteristics of the lightning VLF signals, a flexible signal acquisition method is provided for the low ionosphere. In the embodiments of the present disclosure, using a limited number of lightning VLF signals, high-precision detection of the electron density in the low ionosphere can be achieved, which has important application value for the study of the ionospheric space environment. This method has excellent spatial and temporal resolutions, strong anti-interference ability, and low detection cost, and is of great significance for the remote sensing detection and application of the low ionosphere, especially in the fields of space weather monitoring, communication system optimization, etc., and has a wide range of application prospects.

[0068] Figure 2 The flowchart of the low ionosphere remote sensing detection method provided by an exemplary embodiment of the present disclosure is shown, and this method can be executed by a computer device. Refer to Figure 2 and this method includes: In step 201, multiple lightning very low frequency signals are acquired.

[0069] In step 202, for the i-th lightning very low frequency signal, multiple simulated very low frequency signals corresponding to different reflection heights and different sharpnesses are calculated.

[0070] In step 203, based on the multiple simulated very low frequency signals corresponding to the i-th lightning very low frequency signal, the equivalent average electron density of the i-th lightning very low frequency signal is determined.

[0071] For the relevant content of steps 201 to 203, refer to the aforementioned steps 101 to 103, which are not elaborated here.

[0072] In step 204, based on each lightning very low frequency signal and the equivalent average electron density of each lightning very low frequency signal, a tomography equation is established.

[0073] Optionally, the tomography equation is expressed by the following formula (4).

[0074] (4) In formula (4), represents the propagation path integration matrix of the i-th lightning very low frequency signal, which is calculated based on formula (5), represents the equivalent average electron density of the i-th lightning very low frequency signal, is the quantity to be solved, representing the DCT (Discrete Cosine Transform) coefficient vector of the low ionospheric electron density distribution.

[0075] In the tomography equation, the propagation path of each lightning very low frequency signal is evenly divided into R segments, and the target area is divided into M×N longitude and latitude grids. In this case, the element in the p-th row and q-th column of the propagation path integration matrix of the i-th lightning very low frequency signal is expressed by formula (5).

[0076] (5) In formula (5), is the great circle path length from the lightning to the detection station in the i-th lightning very low frequency signal, is the length of the r-th propagation path segment of the i-th lightning very low frequency signal, where r is an integer and the value range of r is from 1 to R, is the central latitude of the r-th propagation path segment of the i-th lightning very low frequency signal, is the central longitude of the r-th propagation path segment of the i-th lightning very low frequency signal, is a parameter in the inverse discrete cosine transform, which is expressed by formulas (6) to (8).

[0077] (6) (7) (8) The meanings of the parameters in Formulas (6) to (8) are the same as those in Formula (5), and are not elaborated herein.

[0078] Based on Formulas (5) to (8), each element in the propagation path integral matrix of the i-th lightning very low frequency signal can be calculated, thereby obtaining the propagation path integral matrix of the i-th lightning very low frequency signal. Each lightning very low frequency signal can be processed using the above Formulas (5) to (8), so that the propagation path integral matrix of each lightning very low frequency signal can be obtained. Finally, a tomographic scanning equation can be established.

[0079] The essence of Formulas (5) to (8) is to use DCT technology to compress the unknown information volume of the lower ionosphere and then establish a tomographic scanning equation. Through Formulas (5) to (8), the calculation amount of the tomographic scanning equation can be reduced and the calculation efficiency of the tomographic scanning equation can be improved.

[0080] In step 205, solve the tomographic scanning equation to obtain the DCT coefficient vector of the electron density distribution in the lower ionosphere.

[0081] Exemplarily, under the LASSO (Least Absolute Shrinkage and Selection Operator) regularization constraint, use ADMM (Alternating Direction Method of Multipliers) to implement the solution of the tomographic scanning equation.

[0082] There are many implementation methods of LASSO regularization and ADMM in the related technologies, and they are not elaborated herein.

[0083] In step 206, reconstruct the DCT coefficient vector of the electron density distribution in the lower ionosphere into an M×N matrix, and perform an IDCT transform to obtain the tomographic scanning result of the target area.

[0084] There are many implementation methods of the IDCT (Inverse Discrete Cosine Transform) transform in the related technologies, and they are not elaborated herein.

[0085] Exemplarily, use the WWLLN lightning location system to perform tomographic scanning simulation on the electron density distribution in the lower ionosphere with 2172 lightning very low frequency signals and location information collected randomly within 10 minutes on June 15, 2018.

[0086] First, for each lightning very low frequency (VLF) signal, multiple simulated VLF signals corresponding to different reflection heights and different sharpnesses are calculated in the manner of step 102. In this embodiment, the range of the reflection height is 66 - 88 km, with a step size of 2 km; the range of the sharpness is 0.3 - 1 km -1 , with a step size of 0.05 km -1 .

[0087] Then, the equivalent average low ionospheric electron density of each lightning VLF signal is calculated in the manner of step 103.

[0088] Then, based on each lightning VLF signal and the equivalent average electron density of each lightning VLF signal, a tomography equation is established. In this embodiment, when constructing the tomography equation, the propagation path of each lightning VLF signal is evenly divided into 200 segments (i.e., R = 200), the target area is 5°N - 45°N, 60°W - 120°W, and the target area is divided into a 400×600 longitude - latitude grid; Finally, under the LASSO regularization constraint, ADMM is used to solve the above - mentioned tomography equation to obtain the value of the quantity to be solved in the tomography equation ; then, an IDCT transform is performed on the quantity to be solved to obtain the electron density tomography result of the target area. In this embodiment, the LASSO regularization parameter is 1×10 -0.6 .

[0089] Figure 3 is a schematic diagram of the low - ionosphere remote sensing detection result of the target area obtained by the method in the embodiment of the present disclosure, Figure 4 is a schematic diagram of the error of the low - ionosphere remote sensing detection result of the target area obtained by the method in the embodiment of the present disclosure. In Figure 4 , the closer to white, the smaller the error and the higher the accuracy. It can be seen that Figure 4 most of them are white, indicating that the tomography result of the target area obtained by the method in the embodiment of the present disclosure has a high accuracy.

[0090] The following is an apparatus embodiment of the present application. For details not described in detail in the apparatus embodiment, reference may be made to the above - mentioned method embodiment.

[0091] Figure 5 shows a schematic structural diagram of a low - ionosphere remote sensing detection apparatus provided by an exemplary embodiment of the present disclosure. Refer to Figure 5 , the low - ionosphere remote sensing detection apparatus 500 includes: an acquisition module 501, a simulated VLF signal calculation module 502, an equivalent average electron density determination module 503, a tomography equation establishment module 504, and a solution module 505.

[0092] The acquisition module 501 is used to acquire multiple lightning very low frequency signals.

[0093] The analog very low frequency signal calculation module 502 is used to calculate multiple analog very low frequency signals corresponding to different reflection heights and different sharpnesses for the i-th lightning very low frequency signal.

[0094] The equivalent average electron density determination module 503 is used to determine the equivalent average electron density of the i-th lightning very low frequency signal based on multiple analog very low frequency signals corresponding to the i-th lightning very low frequency signal.

[0095] The tomography equation establishment module 504 is used to establish a tomography equation based on each lightning very low frequency signal and the equivalent average electron density of each lightning very low frequency signal.

[0096] The solution module 505 is used to solve the tomography equation to obtain the electron density tomography result of the target area, where the target area is a set area in the wide-area low ionosphere; where i is a positive integer, and the value range of i is from 1 to I, and I is the total number of lightning very low frequency signals.

[0097] Optionally, in the tomography equation establishment module 504, the tomography equation is expressed by the following formula:

[0098] Among them, represents the propagation path integral matrix of the i-th lightning very low frequency signal, represents the equivalent average electron density of the i-th lightning very low frequency signal, is the quantity to be solved, representing the discrete cosine transform coefficient vector of the low ionosphere electron density distribution.

[0099] Optionally, in the tomography equation establishment module 504, the propagation path of each lightning very low frequency signal is evenly divided into R segments, the target area is divided into M×N longitude and latitude grids, and the element in the p-th row and q-th column of the propagation path integral matrix of the i-th lightning very low frequency signal is expressed by the following formula:

[0100] Among them, is the great circle path length from the lightning to the detection station in the i-th lightning very low frequency signal, is the length of the r-th propagation path of the i-th lightning very low frequency signal, r is an integer, and the value range of r is from 1 to R, is the central latitude of the r-th propagation path of the i-th lightning very low frequency signal, is the central longitude of the r-th propagation path of the i-th lightning very low frequency signal, is a parameter in the inverse discrete cosine transform.

[0101] Optionally, the simulated very low frequency (VLF) signal calculation module 502 is further configured to, at a first reflection height and a first sharpness, calculate the simulated VLF signal of the ith lightning VLF signal in the following manner: divide the lightning discharge path from the cloud to the ground corresponding to the ith lightning VLF signal into N segments; based on the lightning current model, calculate the lightning current of each segment of the discharge path of the ith lightning VLF signal, obtaining N lightning currents; perform a Fourier transform on the N lightning current distributions to obtain N lightning current frequency components; based on the first reflection height and the first sharpness, calculate the simulated VLF signal components corresponding to each lightning current frequency component using the long-wave propagation LWPC model, obtaining N simulated VLF signal components; and add the N simulated VLF signal components to obtain the simulated VLF signal of the ith lightning VLF signal at the first reflection height and the first sharpness.

[0102] Optionally, the equivalent average electron density determination module 503 is further configured to: calculate the standard deviation between each lightning simulation signal and the ith lightning VLF signal among the multiple lightning simulation signals corresponding to the ith lightning VLF signal; and calculate the equivalent average electron density of the ith lightning VLF signal based on the reflection height and sharpness corresponding to the lightning simulation signal with the smallest standard deviation.

[0103] Optionally, the equivalent average electron density determination module 503 is further configured to calculate the equivalent average electron density of the ith lightning VLF signal using the following formula:

[0104] where is the equivalent average electron density, is the reflection height, is the sharpness, is the height of the lightning VLF signal from the ground.

[0105] It should be noted that: when the low ionosphere remote sensing detection device provided in the above embodiment performs low ionosphere remote sensing detection, only the division of the above functional modules is used as an example for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the low ionosphere remote sensing detection device provided in the above embodiment and the embodiment of the low ionosphere remote sensing detection method belong to the same concept. For the specific implementation process, please refer to the method embodiment, which will not be elaborated here.

[0106] In the embodiments of the present disclosure, the division of modules is illustrative and is only a logical function division. In actual implementation, there may be other division methods. In addition, in each embodiment of the present disclosure, each functional module may be integrated in a processor, may exist physically alone, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module.

[0107] If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a terminal device (which may be a personal computer, a mobile phone, or a communication device, etc.) or a processor to execute all or part of the steps of the method in each embodiment of the present disclosure. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc that can store program codes.

[0108] Figure 6 It is a schematic structural diagram of a computer device provided by an embodiment of the present disclosure. As Figure 6 shown, the computer device 600 includes: a processor 601 and a memory 602.

[0109] The processor 601 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. The processor 601 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 601 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 601 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 601 may further include an AI (Artificial Intelligence) processor, and the AI processor is used to process computational operations related to machine learning.

[0110] The memory 602 may include one or more computer-readable storage media, and the computer-readable storage media may be non-transitory. The memory 602 may further include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments, the non-transitory computer-readable storage media in the memory 602 is used to store at least one instruction, and the at least one instruction is used to be executed by the processor 601 to implement the low ionosphere remote sensing detection method provided in the embodiments of the present disclosure.

[0111] Those skilled in the art can understand that Figure 6 the structure shown in

[0112] does not constitute a limitation on the computer device 600, and may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component layout.

[0113] The embodiments of the present disclosure further provide a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor of the computer device, the computer device can execute the low ionosphere remote sensing detection method provided in the embodiments of the present disclosure.

[0114] The foregoing are only alternative embodiments of the present disclosure and are not intended to limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for remote sensing detection of the lower ionosphere, characterized in that, The method includes: Obtaining a plurality of lightning very low frequency signals; For the i-th lightning very low frequency signal, calculating a plurality of simulated very low frequency signals corresponding to different reflection heights and different sharpnesses; Based on the plurality of simulated very low frequency signals corresponding to the i-th lightning very low frequency signal, determining the equivalent average electron density of the i-th lightning very low frequency signal; Based on each lightning very low frequency signal and the equivalent average electron density of each lightning very low frequency signal, establishing a tomography equation; Solving the tomography equation to obtain the electron density tomography result of the target area, where the target area is a set area in the wide-area low ionosphere; Wherein, i is a positive integer, and the value range of i is from 1 to I, and I is the total number of lightning very low frequency signals.

2. The method according to claim 1, characterized in that The tomography equation is represented by the following formula: Among them, represents the propagation path integral matrix of the i-th lightning very low frequency signal, represents the equivalent average electron density of the i-th lightning very low frequency signal, is the quantity to be solved, representing the discrete cosine transform coefficient vector of the low ionospheric electron density distribution.

3. The method according to claim 2, characterized in that, In the tomography equation, the propagation path of each lightning very low frequency signal is evenly divided into R segments, and the target area is divided into M×N longitude and latitude grids. The element in the p-th row and q-th column of the propagation path integration matrix of the i-th lightning very low frequency signal is represented by the following formula: Among them, The great circle path length from the lightning to the detection station in the i-th lightning very low frequency signal, is the length of the r-th propagation path of the i-th lightning very low frequency signal, where r is an integer and the value range of r is from 1 to R, is the central latitude of the r-th propagation path of the i-th lightning very low frequency signal, is the central longitude of the r-th propagation path of the i-th lightning very low frequency signal, is a parameter in the inverse discrete cosine transform.

4. The method according to any one of claims 1 to 3, characterized in that The step of calculating, for the i-th lightning very low frequency signal, a plurality of simulated very low frequency signals corresponding to different reflection heights and different sharpnesses includes: At the first reflection height and the first sharpness, the following method is used to calculate the simulated very low frequency signal of the i-th lightning very low frequency signal: Dividing the lightning discharge path from the cloud to the ground corresponding to the i-th lightning very low frequency signal into N segments; Based on the lightning current model, calculating the lightning current of each segment of the N-segment discharge path of the i-th lightning very low frequency signal to obtain N lightning currents; Performing Fourier transform on the N lightning current distributions to obtain N lightning current frequency components; Based on the first reflection height and the first sharpness, using the long-wave propagation LWPC model to calculate the simulated very low frequency signal components corresponding to each lightning current frequency component to obtain N simulated very low frequency signal components; Adding the N simulated very low frequency signal components to obtain the simulated very low frequency signal of the i-th lightning very low frequency signal at the first reflection height and the first sharpness.

5. The method according to any one of claims 1 to 3, characterized in that, The step of determining the equivalent average electron density of the i-th lightning very low frequency signal based on the plurality of simulated very low frequency signals corresponding to the i-th lightning very low frequency signal includes: Calculating the standard deviation between each lightning simulation signal and the i-th lightning very low frequency signal among the plurality of lightning simulation signals corresponding to the i-th lightning very low frequency signal; Based on the reflection height and sharpness corresponding to the lightning simulation signal with the smallest standard deviation, calculating the equivalent average electron density of the i-th lightning very low frequency signal.

6. The method according to claim 5, wherein The equivalent average electron density of the i-th lightning very low frequency signal is calculated by the following formula: Among them, is the equivalent average electron density, is the reflection height, is the sharpness, is the height of the lightning very low frequency signal from the ground.

7. A low ionosphere remote sensing detection device, characterized in that The device includes: An acquisition module for acquiring a plurality of lightning very low frequency signals; A simulated very low frequency signal calculation module for calculating, for the i-th lightning very low frequency signal, a plurality of simulated very low frequency signals corresponding to different reflection heights and different sharpnesses; An equivalent average electron density determination module, configured to determine the equivalent average electron density of the ith lightning very low frequency signal based on a plurality of analog very low frequency signals corresponding to the ith lightning very low frequency signal; A tomographic equation establishment module, configured to establish a tomographic equation based on each lightning very low frequency signal and the equivalent average electron density of each lightning very low frequency signal; A solution module, configured to solve the tomographic equation to obtain a tomographic result of the electron density of a target area, where the target area is a set area in the wide-area low ionosphere; wherein, i is a positive integer, and the value range of i is from 1 to I, and I is the total number of lightning very low frequency signals.

8. A computer device, characterized in that, The computer device includes: a memory and a processor, and at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, At least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor to implement the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, The computer program / instructions, when executed by a processor, implement the method according to any one of claims 1 to 6.

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