Ionized layer absorption effect evaluation method based on multi-source measured data driving

Abstract

An ionosphere absorption effect evaluation method based on multi-source actual measurement data driving comprises the following steps of 1, obtaining and preprocessing observation data of an altimeter, and obtaining the optimal number of sunspots through the data of the altimeter; 2, calculating the electron density on a link through a three-dimensional time-varying ionosphere empirical model; 3, acquiring and preprocessing GRACE satellite observation data, and acquiring optimal solar radio flux and geomagnetic index by using the GRACE satellite data; 4, reconstructing atmospheric density and temperature at different heights on a link by using a global atmospheric empirical model; 5, calculating collision frequency of electrons, ions and neutral particles; and 6, calculating ionosphere absorption attenuation. According to the method disclosed by the invention, the electron density and the atmospheric component temperature and density on the communication link are reconstructed by using the data-driven optimized empirical model, the ionospheric absorption effect modeling on the communication link is finally realized, and a model support can be provided for communication effect evaluation of a radio information system with ultra-short waves and above frequency bands.

Description

Technical Field

[0001] The invention 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 driven by multi-source measured data in this field. Background Art

[0002] The ionosphere is an atmospheric layer located about 60 to 1000 kilometers above the earth's surface. It is divided into the D layer, E layer, and F layer according to altitude. 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 electromagnetic wave energy to be absorbed and attenuated. The degree of absorption is affected by the electron density and the frequency of the radio wave. Since the electron density in the D layer is low and the density of neutral molecules is high, it is the main area where ionospheric absorption occurs (60-100km). 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 frequencies of communication links.

[0003] Ionospheric detection technology is the prerequisite for 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 Observatory (GIRO) that has been established can achieve synchronous ionospheric measurements every 5 to 15 minutes at more than 60 locations around the world. In terms of space-based detection, the GRACE satellite, launched by NASA and the German Aerospace Center (DLR), is 500 kilometers above the ground. It is equipped with an onboard accelerometer, which can calculate and invert the atmospheric density at the satellite's location. It has accumulated data for more than one solar activity cycle.

[0004] Combined with the large amount of measured data generated by various ionospheric observation methods, data-driven has become an important method to improve model accuracy and prediction capabilities. Using ground-based measured data to drive the ionospheric absorption model to obtain the optimal estimate of space environment parameters can improve the accuracy of reconstruction of electron density and atmospheric component temperature and density on the communication link, thereby effectively improving the current reporting results of radio signal strength fading caused by ionospheric absorption.

[0005] As an important part of the solar-terrestrial space, the ionosphere has complex variation characteristics. It is strongly affected by solar and geomagnetic activities, and interacts with the neutral atmosphere to form a complex and changeable system. The radio wave energy loss caused by ionospheric absorption is mainly caused by the collision between electrons and other particles in the ionosphere. Its absorption is related to parameters such as collision frequency, electron temperature, ion temperature, neutral molecule density and electron density. Due to the limitations of observation methods and the complexity of the inherent 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-mentioned high-precision ionospheric parameter results. Combining the intrinsic relationship between space environmental parameters such as the sun and geomagnetism and ionospheric parameters, using ground-based measured data to obtain the optimal space environmental parameters and participate in the ionosphere modeling process to improve the model accuracy is of great significance to the practical application of ionosphere models. Summary of the invention

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

[0007] The present invention adopts the following technical solution:

[0008] An ionospheric absorption effect evaluation method driven by multi-source measured data, the improvement of which is that it includes the following steps:

[0009] Step 1: Acquisition and preprocessing of altimeter observation data, using altimeter data to obtain the optimal sunspot number:

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

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

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

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

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

[0015] Step 16: Calculate the optimal sunspot number using the foF2-driven three-dimensional time-varying ionosphere empirical model

[0016]

[0017] In the above formula, argmin represents the variable value that minimizes the objective function. λ, t are latitude, longitude and altitude respectively, foF2obs is the critical frequency data of the F2 layer observed by the altimeter, foF2 model is the calculated value of the critical frequency of the F2 layer of the CCIR (International Radio Consultative Committee) model, R 12 is the sunspot number;

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

[0019] Step 21: Input the receiving point coordinates, transmitting point coordinates, observation time and the optimal sunspot number obtained in step 16.

[0020] Step 22, outputting the electron density of the observation path using a three-dimensional time-varying ionosphere empirical model;

[0021] Step 3: Acquisition and preprocessing of GRACE satellite observation data, using GRACE satellite data to obtain the optimal solar radiation flux and geomagnetic index:

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

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

[0024] Step 33, extracting the satellite observation time from the atmospheric density observation data and comparing it with the input time;

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

[0026] Step 35, take the observation time and satellite observation position as input, set the search range of F10.7 and Ap value, and obtain the optimal solar radiation flux according to the following formula and geomagnetic index

[0027]

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

[0029] Step 4: Use the global atmospheric empirical model to reconstruct the atmospheric density and temperature at different heights on the link:

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

[0031] Step 42, using the global atmospheric empirical model to output the atmospheric density and temperature of the observation path point;

[0032] Step 5, calculate the collision frequency between electrons, ions and neutral particles:

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

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

[0035] Step 53, calculating the electron-ion collision frequency;

[0036] Step 54, calculating the total effective electron collision frequency;

[0037] Step 6, calculate the ionospheric absorption attenuation:

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

[0039] Step 62, integrally calculate the ionospheric absorption attenuation A of the signal propagation path:

[0040]

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

[0042] Further, in step 22, the three-dimensional time-varying ionosphere empirical model selects the international reference ionosphere model.

[0043] Further, in step 42, the global atmospheric empirical model selects the NRL-MSISE00 model.

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

[0045] The beneficial effects of the present invention are:

[0046] The method disclosed in the present invention adjusts space environment parameters based on altimeter and GRACE satellite observation data, combines a three-dimensional time-varying ionosphere empirical model and a global atmosphere empirical model, and utilizes data-driven optimization of the empirical model to reconstruct the electron density and the temperature and density of atmospheric components on the communication link, thereby ultimately achieving modeling of the ionosphere absorption effect on the communication link, and can provide model support for the communication effect evaluation of radio information systems in ultra-short wave and above frequency bands. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic flow diagram of the method of the present invention;

[0048] Figure 2 It is a schematic diagram of the input and output settings for obtaining the optimal sunspot number driven by measured data;

[0049] Figure 3 This is a schematic diagram of the input and output settings for obtaining the optimal F10.7 and Ap driven by measured data. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] Embodiment 1, this embodiment discloses a method for evaluating ionospheric absorption effect based on multi-source measured data, such as Figure 1 As shown, the following steps are included:

[0052] Step 1: Acquisition and preprocessing of altimeter observation data, and use of altimeter data to obtain the optimal sunspot number parameters:

[0053] Step 11, download the automatic interpretation data of the ionospheric digital altimeter; the data source is the Global Ionospheric Radio Observatory (GIRO);

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

[0055] Step 13, extracting the altimeter coordinates from the data;

[0056] Step 14, selecting the altimeter station according to the coordinates of the receiving points on the observation path;

[0057] Step 15, extracting the critical frequency foF2 of the ionosphere F2 layer at the corresponding time of the altimeter station according to the observation time;

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

[0059]

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

[0061] Step 2, using the three-dimensional time-varying ionosphere empirical model to calculate the electron density on the link;

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

[0063] Step 22, using the three-dimensional time-varying ionosphere empirical model to output the electron density distribution of the observation path; the three-dimensional time-varying ionosphere empirical model selects the International Reference Ionosphere model (IRI), and the model converts the As input parameters;

[0064] Step 3: Acquisition and preprocessing of GRACE satellite observation data, using GRACE satellite data to obtain the optimal solar radiation flux and geomagnetic index:

[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, the coordinates of the receiving point of the observation path, and the coordinates of the transmitting point of the observation link;

[0067] Step 33, extracting the satellite observation time from the atmospheric density observation data and comparing it with the input time;

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

[0069] Step 35, as Figure 3 As shown, take the observation time and satellite observation position as input, set the search range of F10.7 and Ap value, and obtain the optimal solar radiation flux according to the following formula and geomagnetic index

[0070]

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

[0072] Step 4: Use the global atmospheric empirical model to reconstruct the atmospheric density and temperature at different heights on the link:

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

[0074] Step 42, using the global atmospheric empirical model to output the atmospheric density and temperature of the observation path point; the global atmospheric empirical model selects the NRL-MSISE00 model, which converts the optimal solar radiation flux obtained in step 35 and geomagnetic index As input parameters;

[0075] Step 5, calculate the collision frequency between electrons, ions and neutral particles:

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

[0077] Step 52, calculating the collision frequency of electrons and neutral particles, where neutral particles include nitrogen, oxygen, hydrogen, helium, argon, and oxygen atoms;

[0078] Step 53, calculating the electron-ion collision frequency;

[0079] Step 54, calculating the total effective electron collision frequency;

[0080] Step 6, calculate the ionospheric absorption attenuation:

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

[0082] Step 62, integrally calculate the ionospheric absorption attenuation A of the signal propagation path:

[0083]

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

[0085] In summary, the method of the present invention adjusts the space environment parameters based on the altimeter and GRACE satellite observation data, combines the three-dimensional time-varying ionosphere empirical model and the global atmosphere empirical model, and uses data-driven optimization of the empirical model to reconstruct the electron density and the temperature and density of the atmospheric components on the communication link. Finally, the ionospheric absorption effect on the communication link is modeled, which can provide model support for the communication effect evaluation of the radio information system in the ultra-short wave and above frequency bands.

Claims

1. A method for evaluating ionospheric absorption effect based on multi-source measured data, characterized in that: The steps include: Step 1: Acquisition and preprocessing of altimeter observation data, using altimeter data to obtain the optimal sunspot number: Step 11, downloading the automatic interpretation data of the ionospheric digital altimeter; Step 12, input the observation time, the coordinates of the receiving point of the observation path, and the coordinates of the transmitting point of the observation link; Step 13, extracting the altimeter coordinates from the data; Step 14, selecting the altimeter station according to the coordinates of the receiving points on the observation path; Step 15, extracting the critical frequency foF2 of the ionosphere F2 layer at the corresponding time of the altimeter station according to the observation time; Step 16: Calculate the optimal sunspot number using the foF2-driven three-dimensional time-varying ionosphere empirical model In the above formula, argmin represents the variable value that minimizes the objective function. are latitude, longitude and altitude respectively, foF2 obs is the critical frequency data of the F2 layer observed by the altimeter, foF2 model is the calculated value of the critical frequency of the F2 layer of the CCIR model, R 12 is the sunspot number; Step 2, using the three-dimensional time-varying ionosphere empirical model to calculate the electron density on the link; Step 21: Input the receiving point coordinates, transmitting point coordinates, observation time and the optimal sunspot number obtained in step 16. Step 22, outputting the electron density of the observation path using a three-dimensional time-varying ionosphere empirical model; Step 3: Acquisition and preprocessing of GRACE satellite observation data, using GRACE satellite data to obtain the optimal solar radiation flux and geomagnetic index: Step 31, downloading GRACE satellite atmospheric density observation data; Step 32, input the observation time, the coordinates of the receiving point of the observation path, and the coordinates of the transmitting point of the observation link; Step 33, extracting the satellite observation time from the atmospheric density observation data and comparing it with the input time; Step 34, extracting the satellite observation position, altitude, and atmospheric density measured data closest to the input time; Step 35, take the observation time and satellite observation position as input, set the search range of F10.7 and Ap value, and obtain the optimal solar radiation flux according to the following formula and geomagnetic index In the above formula: obs is the atmospheric density data observed by the GRACE satellite, ρ model is the atmospheric density calculated by the global atmospheric empirical model, F10.7 is the solar radio flux, and Ap is the geomagnetic index; Step 4: Use the global atmospheric empirical model to reconstruct the atmospheric density and temperature at different heights on the link: Step 41: Input the latitude and longitude, altitude, observation time, and optimal solar radiation flux obtained in step 35. and geomagnetic index Step 42, using a global atmospheric empirical model to output the atmospheric density and temperature of the observation path point; Step 5, calculate the collision frequency between electrons, ions and neutral particles: Step 51, input electron density, atmospheric density, and atmospheric temperature; Step 52, calculating the electron-neutral particle collision frequency; Step 53, calculating the electron-ion collision frequency; Step 54, calculating the total effective electron collision frequency; Step 6, calculate the ionospheric absorption attenuation: Step 61, input signal frequency, electron density, and total effective electron collision frequency; Step 62, integrally calculate the ionospheric absorption attenuation A of the signal propagation path: In the above formula, f is the signal frequency, N e represents the electron density, ν represents the total effective electron collision frequency, and s is the observation path.

2. The ionospheric absorption effect evaluation method based on multi-source measured data driven according to claim 1, characterized in that: In step 22, the three-dimensional time-varying ionosphere empirical model selects the international reference ionosphere model.

3. The ionospheric absorption effect evaluation method based on multi-source measured data driving according to claim 1 is characterized in that: In step 42, the NRL-MSISE00 model is selected as the global atmospheric empirical model.

4. The ionospheric absorption effect evaluation method based on multi-source measured data driving according to claim 1 is characterized in that: In step 52, the neutral particles include nitrogen, oxygen, hydrogen, helium, argon, and oxygen atoms.

Images