An ionospheric absorption effect evaluation method based on multi-source measured data driving

Abstract

A kind of ionospheric absorption effect evaluation method based on multi-source measured data driving, comprising the following steps: step 1, obtaining and preprocessing of altimeter observation data, obtaining optimal sunspot number using altimeter data;Step 2, calculating the electron density on the link using three-dimensional time-varying ionospheric empirical model;Step 3, obtaining and preprocessing of GRACE satellite observation data, obtaining optimal solar radio flux and geomagnetic index using GRACE satellite data;Step 4, reconstructing atmospheric density and temperature at different altitudes on the link using global atmospheric empirical model;Step 5, calculating electron and ion, neutral particle collision frequency;Step 6, calculating ionospheric absorption attenuation.The method disclosed in the application optimizes empirical model using data driving to reconstruct electron density, atmospheric composition temperature and density on the communication link, and finally realizes modeling of ionospheric absorption effect on the communication link, which can provide model support for communication effect evaluation of ultra-short wave and above frequency band radio information system.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of space environment monitoring and radio communication technology of aerospace science, and particularly relates to an ionospheric absorption effect evaluation method based on multi-source measured data driving in the field. BACKGROUND

[0002] The ionosphere is an atmospheric layer located above the earth's surface at about 60-1000 kilometers, which is divided into D layer, E layer and F layer according to the height, and there are various free electrons and ions in it. When electromagnetic waves pass through the ionosphere, the collision between free electrons and neutral particles and ions in the ionosphere causes the energy of electromagnetic waves to be absorbed, resulting in attenuation. The absorption degree is affected by the electron density and the frequency of the wave. Since the electron density of the D layer is low and the neutral molecular density is large, the D layer is the main area (60-100 km) where ionospheric absorption occurs. Establishing an ionospheric absorption model has important application value for evaluating the communication reliability of different frequency bands of radio communication systems and predicting the available communication frequency of communication link.

[0003] Ionospheric detection technology is the premise of obtaining rich ionospheric observation data and subsequent ionospheric research. In terms of ground-based detection, ionospheric altimeter is one of the main detection means. The global ionospheric radio observation network (GIRO) has been established, which can realize ionospheric synchronous measurement every 5-15 minutes at more than 60 locations around the world. In terms of space-based detection, the GRACE satellite launched by NASA and DLR can obtain atmospheric density at the satellite location by calculation and inversion, and has accumulated more than one solar cycle of data.

[0004] Combining the large amount of measured data generated by the current various ionospheric observation methods, data driving has become an important method to improve the precision and prediction ability of the model. Using the ground and space-based measured data to drive the ionospheric absorption model to obtain the optimal estimation of the space environment parameters can improve the reconstruction precision of the electron density and the atmospheric composition temperature and density on the communication link, thereby effectively improving the present report result of the radio signal strength fading caused by ionospheric absorption.

[0005] As an important part of the space between the sun and the earth, the ionosphere has complex variation characteristics, which is strongly affected by solar and geomagnetic activities and interacts with the neutral atmosphere to form a complex and variable system. The radio wave energy loss caused by ionospheric absorption is mainly due to the collision between electrons and other particles in the ionosphere, and the absorption is related to the collision frequency, electron temperature, ion temperature, neutral molecular density and electron density. Due to the limitations of observation means and the complexity of the internal physical mechanism of the ionosphere, the degree of characterization of the ionosphere state using only empirical models and theoretical models is limited, and it is difficult to obtain the above high-precision ionospheric parameter results. Combined with the internal relationship between the space environment parameters such as the sun and the geomagnetic and the ionospheric parameters, the optimal space environment parameters are obtained by using the ground-based and space-based observation data, and are used in the ionospheric modeling process to improve the model precision, which is of great significance for the application of the ionospheric model in practice. SUMMARY

[0006] The technical problem to be solved by the present application is to provide an ionospheric absorption effect evaluation method based on multi-source measured data driving.

[0007] The present application adopts the following technical scheme:

[0008] An ionospheric absorption effect evaluation method based on multi-source measured data driving, the improvement lies in comprising the following steps:

[0009] Step 1, obtaining and preprocessing the observation data of the altimeter, and obtaining the optimal sunspot number by using the altimeter data:

[0010] Step 11, downloading the ionospheric digital altimeter automatic interpretation data;

[0011] Step 12, inputting the observation time, observation path receiving point coordinates and observation link transmitting point coordinates;

[0012] Step 13, extracting the altimeter coordinates from the data;

[0013] Step 14, selecting the altimeter station according to the observation path receiving point coordinates;

[0014] Step 15, extracting the ionospheric F2 layer critical frequency foF2 corresponding to the observation time of the altimeter station according to the observation time;

[0015] Step 16, calculating the optimal sunspot number by using the foF2 to drive the three-dimensional time-varying ionospheric empirical model

[0016]

[0017] In the formula, argmin represents the variable value when the objective function is minimized, λ, t are latitude, longitude and height, respectively, and foF2obs F2layer critical frequency data observed by ionosonde, foF2 model F2layer critical frequency calculated value for CCIR (International Radio Consulting Committee) model, R 12 Sunspot number;

[0018] Step 2, calculate the electron density on the link by using the three-dimensional time-varying ionospheric empirical model;

[0019] Step 21, input the observed path receiving point coordinates, transmitting point coordinates, observation time, and the optimal sunspot number obtained in step 16

[0020] Step 22, output the observed path electron density by using the three-dimensional time-varying ionospheric empirical model;

[0021] Step 3, GRACE satellite observation data acquisition and preprocessing, use GRACE satellite data to obtain the optimal solar radio flux and geomagnetic index:

[0022] Step 31, download the GRACE satellite atmospheric density observation data;

[0023] Step 32, input the observation time, observed path receiving point coordinates, and observed link transmitting point coordinates;

[0024] Step 33, compare the satellite observation time extracted from the atmospheric density observation data with the input time;

[0025] Step 34, extract the satellite observation position, height, and atmospheric density measured data closest to the input time;

[0026] Step 35, input the observation time and satellite observation position, set the search range of F10.7 and Ap value, and obtain the optimal solar radio flux and geomagnetic index

[0027]

[0028] In the above formula: ρ obs is the atmospheric density data observed by GRACE satellite, ρ model is the calculated value of atmospheric density of global atmospheric empirical model, F10.7 is the solar radio flux, and Ap is the geomagnetic index;

[0029] Step 4, reconstruct the atmospheric density and temperature at different altitudes on the link by using the global atmospheric empirical model:

[0030] Step 41: Input the latitude, longitude, and altitude of the observation path point, the observation time, and the optimal solar radio flux obtained in Step 35. and geomagnetic index

[0031] Step 42: Utilize the global atmospheric empirical model to output the atmospheric density and temperature at the observation path points;

[0032] Step 5, calculate the collision frequencies of electrons and ions, and neutral particles:

[0033] Step 51: Input electron density, atmospheric density, and atmospheric temperature;

[0034] Step 52, calculate the electron-neutral particle collision frequency;

[0035] Step 53: Calculate the electron-ion collision frequency;

[0036] Step 54: Calculate the total effective electron collision frequency;

[0037] Step 6, calculate ionospheric absorption attenuation:

[0038] Step 61: Input signal frequency, electron density, and total effective electron collision frequency;

[0039] Step 62, Integrate and calculate the ionospheric absorption attenuation A along the signal propagation path:

[0040]

[0041] In the above formula, f is the signal frequency, and N e Let represent the electron density, v represent the total effective electron collision frequency, and s represent the observation path.

[0042] Furthermore, in step 22, the international reference ionospheric model is selected as the three-dimensional time-varying ionospheric empirical model.

[0043] Furthermore, in step 42, the global atmospheric empirical model selected is the NRL-MSISE00 model.

[0044] Furthermore, in step 52, the neutral particles include nitrogen, oxygen, hydrogen, helium, argon, and oxygen atoms.

[0045] The beneficial effects of this invention are:

[0046] The method disclosed in this invention adjusts space environment parameters based on altimeter and GRACE satellite observation data, combines a three-dimensional time-varying ionospheric empirical model and a global atmospheric empirical model, and uses data-driven optimization of the empirical model to reconstruct electron density and atmospheric composition temperature and density on the communication link. Ultimately, it achieves modeling of the ionospheric absorption effect on the communication link, which can provide model support for the evaluation of communication effects of radio information systems in the ultra-shortwave and above frequency bands. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating the method of the present invention;

[0048] Figure 2 This is a schematic diagram of the input and output settings for obtaining the optimal sunspot number based on measured data.

[0049] Figure 3 This is a schematic diagram illustrating the optimal input / output settings for F10.7 and Ap, driven by actual measured data. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0051] Example 1 discloses a method for evaluating ionospheric absorption effects based on multi-source measured data, such as... Figure 1 As shown, it includes the following steps:

[0052] Step 1: Acquisition and preprocessing of altimeter observation data; obtaining the optimal sunspot number parameters using altimeter data:

[0053] Step 11: Download the ionospheric digital altimeter automatic interpretation data; the data source is the Global Ionospheric Radio Observatory (GIRO).

[0054] Step 12: Input the observation time, coordinates of the receiving point along the observation path, and coordinates of the transmitting point along the observation link;

[0055] Step 13: Extract the altimeter coordinates from the data;

[0056] Step 14: Select the altimeter station based on the coordinates of the receiving point along the observation path;

[0057] Step 15: Extract the critical frequency foF2 of the F2 layer of the ionosphere at the corresponding time of the observation of the altimeter station;

[0058] Step 16, as follows Figure 2 As shown, the optimal sunspot number is calculated using a three-dimensional time-varying empirical model of the ionosphere driven by foF2.

[0059]

[0060] In the above formula, argmin represents the value of the variable that minimizes the objective function. λ and t represent the latitude, longitude, and altitude corresponding to the foF2 observation, respectively. obs The critical frequency data of layer F2 observed by the altimeter, foF2 model R is the calculated critical frequency for layer F2 of the CCIR (International Radio Consulting Committee) model. 12 The number of sunspots;

[0061] Step 2: Calculate the electron density on the link using a three-dimensional time-varying ionospheric empirical model;

[0062] Step 21: Input the coordinates of the receiving point, the transmitting point, the observation time, and the optimal sunspot number obtained in Step 16.

[0063] Step 22: Output the electron density distribution along the observation path using a three-dimensional time-varying ionospheric empirical model; the selected three-dimensional time-varying ionospheric empirical model is the International Reference Ionosphere (IRI) model, which will use the electron density distribution obtained in step 16. As input parameters;

[0064] Step 3: Acquisition and preprocessing of GRACE satellite observation data; obtaining optimal solar radio flux and geomagnetic index using GRACE satellite data:

[0065] Step 31: Download the GRACE satellite atmospheric density observation data; the data download link is ftp: / / thermosphere.tudelft.nl / .

[0066] Step 32: Input the observation time, coordinates of the receiving point along the observation path, and coordinates of the transmitting point along the observation link;

[0067] Step 33: Extract the satellite observation time from the atmospheric density observation data and compare it with the input time;

[0068] Step 34: Extract the measured satellite observation position, altitude, and atmospheric density data closest to the input time;

[0069] Step 35, as follows Figure 3 As shown, the observation time and satellite observation position are used as inputs, and the search ranges for F10.7 and Ap values ​​are set. The optimal solar radio flux is obtained according to the following formula. and geomagnetic index

[0070]

[0071] In the above formula: arg min represents the value of the variable that minimizes the objective function. λ and t represent the latitude, longitude, and altitude corresponding to the density observation values, respectively, and ρ obs For atmospheric density data observed by the GRACE satellite, ρ model The atmospheric density is calculated using a global atmospheric empirical model, F10.7 is the solar radio flux, and Ap is the geomagnetic index.

[0072] Step 4: Reconstruct atmospheric density and temperature at different altitudes along the link using a global atmospheric empirical model:

[0073] Step 41: Input the latitude, longitude, and altitude of the observation path point, the observation time, and the optimal solar radio flux obtained in Step 35. and geomagnetic index

[0074] Step 42: Output the atmospheric density and temperature at the observation path points using the global atmospheric empirical model; the NRL-MSISE00 model is selected as the global atmospheric empirical model, and the model will output the optimal solar radio flux obtained in step 35. and geomagnetic index As input parameters;

[0075] Step 5, calculate the collision frequencies of electrons and ions, and neutral particles:

[0076] Step 51: Input electron density, atmospheric particle density, and atmospheric temperature;

[0077] Step 52: Calculate the electron-neutral particle collision frequency. Neutral particles include nitrogen, oxygen, hydrogen, helium, argon, and oxygen atoms.

[0078] Step 53: Calculate the electron-ion collision frequency;

[0079] Step 54: Calculate the total effective electron collision frequency;

[0080] Step 6, calculate ionospheric absorption attenuation:

[0081] Step 61: Input signal frequency, electron density, and total effective electron collision frequency;

[0082] Step 62, Integrate and calculate the ionospheric absorption attenuation A along the signal propagation path:

[0083]

[0084] In the above formula, f is the signal frequency, and N e Let ν represent the electron density, ν represent the total effective electron collision frequency, and s represent the observation path.

[0085] In summary, the method of this invention adjusts space environment parameters based on altimeter and GRACE satellite observation data, combines a three-dimensional time-varying ionospheric empirical model and a global atmospheric empirical model, and uses data-driven optimization of the empirical model to reconstruct electron density and atmospheric composition, temperature, and density on the communication link. Ultimately, it achieves modeling of the ionospheric absorption effect on the communication link, and can provide model support for the evaluation of communication effects of radio information systems in the UHF and above frequency bands.

Claims

1. A method for evaluating ionospheric absorption effects based on multi-source measured data, characterized in that, Includes the following steps: Step 1: Acquisition and preprocessing of altimeter observation data; obtaining the optimal sunspot number using altimeter data: Step 11: Download the data automatically interpreted by the ionospheric digital altimeter; Step 12: Input the observation time, coordinates of the receiving point along the observation path, and coordinates of the transmitting point along the observation link; Step 13: Extract the altimeter coordinates from the data; Step 14: Select the altimeter station based on the coordinates of the receiving point along the observation path; Step 15: Extract the critical frequency foF2 of the F2 layer of the ionosphere at the corresponding time of the observation of the altimeter station; Step 16: Calculate the optimal sunspot number using a foF2-driven three-dimensional time-varying ionospheric empirical model. In the above formula, argmin represents the value of the variable that minimizes the objective function. These represent latitude, longitude, and altitude, respectively. foF2 obs The critical frequency data of layer F2 observed by the altimeter, foF2 model R is the calculated critical frequency of the F2 layer in the CCIR model. 12 The number of sunspots; Step 2: Calculate the electron density on the link using a three-dimensional time-varying ionospheric empirical model; Step 21: Input the coordinates of the receiving point, the transmitting point, the observation time, and the optimal sunspot number obtained in Step 16. Step 22: Utilize the three-dimensional time-varying ionospheric empirical model to output the electron density along the observation path; Step 3: Acquisition and preprocessing of GRACE satellite observation data; obtaining optimal solar radio flux and geomagnetic index using GRACE satellite data: Step 31: Download atmospheric density observation data from the GRACE satellite; Step 32: Input the observation time, coordinates of the receiving point along the observation path, and coordinates of the transmitting point along the observation link; Step 33: Extract the satellite observation time from the atmospheric density observation data and compare it with the input time; Step 34: Extract the measured satellite observation position, altitude, and atmospheric density data closest to the input time; Step 35: Using the observation time and satellite observation position as input, set the search range for F10.7 and Ap values, and obtain the optimal solar radio flux using the following formula. and geomagnetic index In the above formula: ρ obs For atmospheric density data observed by the GRACE satellite, ρ model The atmospheric density is calculated using a global atmospheric empirical model, F10.7 is the solar radio flux, and Ap is the geomagnetic index. Step 4: Reconstruct atmospheric density and temperature at different altitudes along the link using a global atmospheric empirical model: Step 41: Input the latitude, longitude, and altitude of the observation path point, the observation time, and the optimal solar radio flux obtained in Step 35. and geomagnetic index Step 42: Utilize the global atmospheric empirical model to output the atmospheric density and temperature at the observation path points; Step 5, calculate the collision frequencies of electrons and ions, and neutral particles: Step 51: Input electron density, atmospheric density, and atmospheric temperature; Step 52, calculate the electron-neutral particle collision frequency; Step 53: Calculate the electron-ion collision frequency; Step 54: Calculate the total effective electron collision frequency; Step 6, calculate ionospheric absorption attenuation: Step 61: Input signal frequency, electron density, and total effective electron collision frequency; Step 62, Integrate and calculate the ionospheric absorption attenuation A along the signal propagation path: In the above formula, f is the signal frequency, and N e Let ν represent the electron density, ν represent the total effective electron collision frequency, and s represent the observation path.

2. The method for evaluating ionospheric absorption effects based on multi-source measured data as described in claim 1, characterized in that: In step 22, the international reference ionospheric model is selected as the three-dimensional time-varying empirical model for the ionosphere.

3. The method for evaluating ionospheric absorption effects based on multi-source measured data as described in claim 1, characterized in that: In step 42, the global atmospheric empirical model selected is the NRL-MSISE00 model.

4. The method for evaluating ionospheric absorption effects based on multi-source measured data as described in claim 1, characterized in that: In step 52, neutral particles include nitrogen, oxygen, hydrogen, helium, argon, and oxygen atoms.

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