Improved ionized layer TEC estimation method and system based on residual network, and storage medium

Abstract

The invention provides an improved ionosphere TEC estimation method and system based on a residual network and a storage medium, relates to the technical field of ionosphere monitoring and modeling, and aims to solve the problem that high-precision ionosphere TEC estimation is difficult to obtain due to the fact that atmospheric delay and upper ionosphere delay are not considered in an existing GNSS-R method. Comprising the following steps: step 1, acquiring a reflection signal through GNSS-R observation data, and acquiring measurement DDM data; 2, atmospheric delay and upper ionized layer delay are considered, and simulation DDM data are generated; step 3, correcting errors of the simulation DDM data and the measurement DDM data by using a residual network; and step 4, based on the corrected DDM data, calculating the ionized layer delay after the atmospheric delay is removed, and further calculating the ionized layer TEC. According to the invention, by considering the atmospheric delay and the delay at the top of the ionosphere, the influence of the factors on the TEC estimation is effectively reduced, and the precision of the TEC estimation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ionospheric monitoring and modeling, and in particular to an improved ionospheric TEC estimation method, system and storage medium based on a residual network. Background Art

[0002] The estimation of the total electron content (TEC) of the ionosphere is an important topic in space weather research. Traditional TEC estimation methods mostly rely on ground station networks. However, due to the lack of data-scarce areas such as the ocean, the application effect of traditional methods in these areas is poor. In recent years, the Global Navigation Satellite System Reflectometry (GNSS-R), as an emerging remote sensing technology, has provided new ideas and perspectives for the inversion of ionospheric parameters. When estimating TEC, existing GNSS-R methods often fail to fully consider the effects of atmospheric delay and upper ionospheric delay, resulting in certain errors in TEC results.

[0003] In addition, with the continuous development of artificial intelligence and machine learning technologies, it is also possible to explore the application of these technologies in the processing and analysis of GNSS-R data to improve the accuracy and reliability of TEC estimation. For example, deep learning models can be used to learn features from massive GNSS-R data and then reconstruct missing or erroneous data, thereby improving the overall accuracy of TEC estimation.

[0004] In summary, although GNSS-R technology provides new ideas for the inversion of ionospheric parameters, it still needs to be continuously optimized and improved in practical applications to more accurately estimate ionospheric parameters such as TEC, especially in data-scarce areas such as the ocean. Summary of the invention

[0005] The technical problems to be solved by the present invention are:

[0006] Existing GNSS-R methods do not take into account atmospheric delay and upper ionospheric delay, making it difficult to obtain high-precision ionospheric TEC estimates.

[0007] The present invention adopts the following technical solutions to solve the above technical problems:

[0008] The present invention provides an improved ionospheric TEC estimation method based on a residual network, comprising the following steps:

[0009] Step 1. Obtain reflection signals through GNSS-R observation data and obtain measurement DDM data;

[0010] Step 2. Considering the atmospheric delay and upper ionospheric delay, generate simulated DDM data;

[0011] Step 3. Use the residual network to correct the errors of the simulated DDM data and the measured DDM data;

[0012] Step 4. Based on the corrected DDM data, the ionospheric delay after removing the atmospheric delay is calculated, and the ionospheric TEC is further calculated.

[0013] Furthermore, step 2 includes the following steps:

[0014] Construct a ZV model of reflected signal power distribution, calculate the time delay and Doppler frequency shift through the ZV model, and generate simulated DDM data;

[0015] The scattering characteristic data of the sea surface is calculated by combining the wind speed distribution and the sea surface slope to further improve the accuracy of the simulated DDM data.

[0016] Furthermore, the ZV model in step 2 is specifically:

[0017]

[0018] Among them, |Y(τ,f D )| 2 represents the received scattered signal power, τ is the delay, and f D is the Doppler shift, T i is the coherent integration time, λ is the wavelength of the signal, P T is the transmission power of the transmitted signal, is the displacement vector from the mirror point to the reflection point, and denote the transmitter and receiver antenna gains respectively, is the surface scattering coefficient; and are the distance from the facet to the receiver and the distance from the transmitter to the facet on the surface, respectively; 2 (τ,f D ) represents the Woodward ambiguity function of the delay and Doppler coordinates of the surface facet:

[0019] χ(τ,f D )=Λ(τ)|sinc(πT i f D )|

[0020]

[0021] where τ c Indicates the length of a code chip.

[0022] Furthermore, step 4 includes the following process:

[0023] Step 4-1. Calculate the ionospheric delay:

[0024] I [m] =DR -T Ra -T Rb

[0025] Among them, D R is the total delay of the reflection path; T Ra and T Rb are the ionospheric delays caused by the reflected path and the direct path, respectively;

[0026] Step 4-2. Perform unit conversion and formula:

[0027]

[0028] Among them I Ra and I Rb is the ionospheric slant TEC of the reflected path and the direct path, in TECU, f [GHz] is the frequency of the GNSS signal in GHz, which is used to convert the delay into the actual value of TEC;

[0029] Step 4-3. Calculate the vertical TEC (VTEC):

[0030]

[0031] Among them, I C is the electron content at the top of the ionosphere, m(z) is used to convert oblique TEC to vertical TEC, R e is the radius of the Earth, H is the height of the ionosphere, α m is the angular factor related to the geometric relationship between the satellite and the receiver, and z is the zenith distance.

[0032] The present invention provides an improved ionospheric TEC estimation system based on a residual network. The system has a program module corresponding to the steps of the method described in any one of the above technical solutions, and executes the steps in the above improved ionospheric TEC estimation method based on a residual network during operation.

[0033] The present invention also provides a computer-readable storage medium, which stores a computer program, and the computer program is configured to implement the steps in the improved ionospheric TEC estimation method based on residual network described in any one of the above technical solutions when called by a processor.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention provides an improved ionospheric TEC estimation method based on a residual network. First, based on the physical model, DDM simulation can accurately simulate the propagation characteristics of the GNSS reflected signal, providing a reliable theoretical basis for the estimation of ionospheric TEC. Secondly, by considering the atmospheric delay and the delay at the top of the ionosphere, the influence of these factors on the TEC estimation is effectively reduced, and the estimation accuracy is improved. It ensures that the estimation of ionospheric TEC is more accurate, especially in the ocean and data-scarce areas, with higher applicability and reliability.

[0036] The present invention also uses a residual network to process the difference between the measured DDM and the simulated DDM, especially in the face of complex signals and high noise. By learning the residuals through the residual network instead of direct fitting, the matching process can be automatically optimized, reducing the need for manual parameter adjustment, thereby improving the adaptability to nonlinear relationships. Useful information is effectively extracted from the original data, improving the generalization ability and robustness of the model, especially when dealing with atmospheric delays and ionosphere top delays, which can reduce the impact of these factors on TEC estimation. The fine adjustment of the difference between DDM simulation and measurement data is retained, and the adaptability and accuracy of the overall method in different environments are enhanced through the residual network. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of the GNSS-R based ionospheric TEC estimation method in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the scheme of the present invention, exemplary implementations or embodiments of the present invention will be described below in conjunction with the accompanying drawings. Obviously, the described implementations or embodiments are only implementations or embodiments of a part of the present invention, not all of them. Based on the implementations or embodiments of the present invention, all other implementations or embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0040] Specific implementation scheme 1: The present invention provides an improved ionospheric TEC estimation method based on a residual network, comprising the following steps:

[0041] Step 1. Obtain reflection signals through GNSS-R observation data and obtain measurement DDM data;

[0042] Step 2. Considering the atmospheric delay and upper ionospheric delay, generate simulated DDM data;

[0043] Step 3. Use the residual network to correct the errors of the simulated DDM data and the measured DDM data;

[0044] Step 4. Based on the corrected DDM data, the ionospheric delay after removing the atmospheric delay is calculated, and the ionospheric TEC is further calculated.

[0045] Specific implementation plan 2: Step 1 includes the following process:

[0046] Obtaining reflected signal Y through GNSS-R observation data M , generating a delay-Doppler map (DDM). The relationship between the delay and Doppler frequency in the DDM is calculated by the ZV model, which represents the simulated DDM.

[0047] By acquiring the position information and speed of the receiver (T) and the transmitter (R), the coordinates and speed of the mirror point (SP), that is, the strongest signal reflection point, are calculated; with the mirror point as the center, a glittering zone (Glistening Zone) is determined, which indicates the range of the sea surface where the signal may be reflected; and the elevation angle of the satellite is calculated. The rest of this implementation is the same as the first implementation.

[0048] Specific implementation plan three: Step 2 includes the following steps:

[0049] Based on the reflection situation in the flash area, the Zavorotny-Voronovich, ZV model of reflected signal power distribution is constructed, and the scattered GNSS signal power is described as a function of time delay and Doppler frequency shift, receiver altitude, transmitter elevation angle and surface scattering coefficient. The ZV model is used to calculate the delay τ and Doppler frequency shift f D Perform calculations to generate simulated DDM data Y similar to the actual signal S , providing data support for subsequent ionospheric TEC estimation.

[0050] The simulated DDM includes the signal delay, Doppler shift, and other influencing factors such as the coherent integration time T c , the wavelength of the signal λ, the transmission power P of the transmitted signal t .

[0051] The relationship between the average square slope of the sea surface and the wind speed is calculated by combining the Cox and Munk models, and the scattering coefficient of the reflected signal (BRCS) is calculated; at the same time, the Fresnel coefficient and scattering vector are calculated, which affect the scattering characteristics of the signal. Then, the signal integration time is determined by the sampling rate and the coherent integration time. Further improve the accuracy of DDM simulation.

[0052] The Woodward ambiguity function (WAF) is used to describe the distribution of the signal at each point in time (delay) and frequency (Doppler shift), and finally a two-dimensional delay-Doppler map is generated. The rest of this embodiment is the same as the second embodiment.

[0053] Specific implementation scheme 4: The ZV model described in step 2 is specifically:

[0054]

[0055] Among them, |Y(τ,f D )| 2 represents the received scattered signal power, τ is the delay, and f D is the Doppler shift, T i is the coherent integration time, λ is the wavelength of the signal, P T is the transmission power of the transmitted signal, is the displacement vector from the mirror point to the reflection point, and denote the transmitter and receiver antenna gains respectively, is the surface scattering coefficient, which is related to the transmitter elevation angle; and are the distance from the small plane to the receiver and the distance from the transmitter to the small plane on the surface, respectively, which are related to the height of the receiver; 2 (τ,f D ) represents the Woodward ambiguity function of the delay and Doppler coordinates of the surface facet:

[0056] χ(τ,f D )=Λ(τ)|sinc(πT i f D )|

[0057]

[0058] where τ c Indicates the length of a code chip. The rest of this implementation is the same as the specific implementation three.

[0059] Specific implementation scheme 5: In step 3, the error between the simulated DDM data and the measured DDM data is learned through the residual network to accurately correct the signal; the output signal of the residual network is expressed as:

[0060] F=g+r

[0061] Among them, F is the actual observed measured DDM data, g is the calculated simulated DDM data, and r is the error (residual) learned by the residual network.

[0062] The training goal of the residual network is to optimize the network parameters by minimizing the prediction error. The error function is:

[0063]

[0064] Among them, Y M is the measured DDM, Y S For simulated DDM, R(x) is the corrected residual, which adjusts the simulated DDM data to more closely match the measured DDM data.

[0065] The optimized best simulated DDM value, optimal delay τ, and Doppler frequency shift f are obtained. D and scaling factor α. The rest of this implementation is the same as the fourth implementation.

[0066] Specific implementation plan six: Step 4 includes the following process:

[0067] Step 4-1. Calculate the ionospheric delay:

[0068] I [m] =D R -T Ra -T Rb

[0069] Among them, D R is the total delay of the reflection path; T Ra and T Rb are the ionospheric delays caused by the reflected path and the direct path, respectively;

[0070] Step 4-2. Perform unit conversion and formula:

[0071]

[0072] Among them I Ra and I Rb is the ionospheric slant TEC of the reflected path and the direct path, in TECU, f [GHz] is the frequency of the GNSS signal in GHz, which is used to convert the delay into the actual value of TEC;

[0073] Step 4-3. Calculate the vertical TEC (VTEC):

[0074]

[0075] Among them, I C is the electron content at the top of the ionosphere, m(z) is used to convert oblique TEC to vertical TEC to compensate for the curvature of the signal path; R e is the radius of the Earth, H is the height of the ionosphere, α mis an angle factor related to the geometric relationship between the satellite and the receiver, and z is the zenith distance (ie, the angle between the satellite and the receiver). The rest of this implementation is the same as the fifth implementation.

[0076] Finally, the performance of the model is evaluated by comparing it with the existing ionospheric model and GNSS observed TEC data.

[0077] The improved ionospheric TEC estimation method (algorithm) based on the residual network proposed in the present invention is the underlying technical core of the present invention, and various products can be derived based on the algorithm.

[0078] Based on the method proposed in the present invention, an improved ionospheric TEC estimation system based on a residual network is developed using a programming language. The system has a program module corresponding to the steps of the above-mentioned technical solution, and executes the steps in the above-mentioned improved ionospheric TEC estimation method based on a residual network during operation.

[0079] The computer program of the developed system (software) is stored on a computer-readable storage medium, and the computer program is configured to implement the steps of the above-mentioned improved ionospheric TEC estimation method based on residual network when called by a processor, that is, the present invention is materialized on a carrier to become a computer program product.

[0080] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0081] The computer programs (also referred to as programs, software, software applications, or codes) of the present invention include machine instructions for programmable processors, and these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or device (e.g., disk, optical disk, memory, programmable logic device PLD) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0082] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. An improved ionospheric TEC estimation method based on residual network, characterized in that: The steps include: Step 1. Obtain reflection signals through GNSS-R observation data and obtain measurement DDM data; Step 2. Considering the atmospheric delay and upper ionospheric delay, generate simulated DDM data; Step 3. Use the residual network to correct the errors of the simulated DDM data and the measured DDM data; Step 4. Based on the corrected DDM data, the ionospheric delay after removing the atmospheric delay is calculated, and the ionospheric TEC is further calculated.

2. The improved ionospheric TEC estimation method based on residual network according to claim 1, characterized in that: Step 2 includes the following steps: Construct a ZV model of reflected signal power distribution, calculate the time delay and Doppler frequency shift through the ZV model, and generate simulated DDM data; The scattering characteristic data of the sea surface is calculated by combining the wind speed distribution and the sea surface slope to further improve the accuracy of the simulated DDM data.

3. The improved ionospheric TEC estimation method based on residual network according to claim 2, characterized in that: The ZV model described in step 2 is specifically: Among them, |Y(τ,f D )| 2 represents the received scattered signal power, τ is the delay, and f D is the Doppler shift, T i is the coherent integration time, λ is the wavelength of the signal, P T is the transmission power of the transmitted signal, is the displacement vector from the mirror point to the reflection point, and denote the transmitter and receiver antenna gains respectively, is the surface scattering coefficient; and are the distance from the facet to the receiver and the distance from the transmitter to the facet on the surface, respectively; 2 (τ,f D ) represents the Woodward ambiguity function of the delay and Doppler coordinates of the surface facet: x(T,f D )=Λ(τ)|sinc(πT i f D )| where τ c Indicates the length of a code chip.

4. The improved ionospheric TEC estimation method based on residual network according to claim 3, characterized in that: Step 4 includes the following process: Step 4-1. Calculate the ionospheric delay: I [m] =D R -T Ra -T Rb Among them, D R is the total delay of the reflection path; T Ra and T Rb are the ionospheric delays caused by the reflected path and the direct path, respectively; Step 4-2. Perform unit conversion and formula: Among them I Ra and I Rb is the ionospheric slant TEC of the reflected path and the direct path, in TECU, f [GHz] is the frequency of the GNSS signal in GHz, which is used to convert the delay into the actual value of TEC; Step 4-3. Calculate the vertical TEC (VTEC): Among them, I C is the electron content at the top of the ionosphere, m(z) is used to convert oblique TEC to vertical TEC, R e is the radius of the Earth, H is the height of the ionosphere, α m is the angular factor related to the geometric relationship between the satellite and the receiver, and z is the zenith distance.

5. An improved ionospheric TEC estimation system based on residual network, characterized in that: The system has a program module corresponding to the steps of the method described in any one of claims 1 to 4, and executes the steps of the improved ionospheric TEC estimation method based on residual network when running.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps in the improved ionospheric TEC estimation method based on residual network described in any one of claims 1 to 4 when called by a processor.

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