Ionized layer three-dimensional electron density calculation method based on radar detection area

Abstract

The invention provides an ionized layer three-dimensional electron density calculation method based on a radar detection area. According to the technical scheme, the method comprises the following steps of: acquiring electron density parameters and geographic coordinates of a detected point in an ISR detection area by utilizing the ISR, and recording the electron density parameters and the geographic coordinates as ISR observation values; an IRI2020 model is utilized to obtain electron density parameters and geographic coordinates of a detected point, and the electron density parameters and the geographic coordinates are recorded as IRI empirical values. And calculating IRI empirical values of non-detected points in the ISR detection area by using an IRI2020 model, inputting the IRI empirical values into the trained neural network to obtain ISR observation values, and marking the non-detected points as detected points. And the step 3 to the step 4 are repeated until the number of the detected points meets the set threshold requirement. According to the invention, the disadvantage of insufficient ISR detection spatial resolution in the background technology is overcome, and the electron density distribution of any spatial resolution in the area can be obtained.

Description

Technical Field

[0001] The invention relates to the technical field of ionosphere detection, and in particular to a method for calculating the ionosphere electron density in a radar detection area. Background Art

[0002] Affected by solar radiation and cosmic rays, the Earth's atmosphere becomes ionized at an altitude of 60 to 1000 km. This region is called the ionosphere. There are a large number of electrons and ions in the ionosphere, and the electron density is generally used to characterize the state of the ionosphere. When electromagnetic waves emitted from the ground to the air pass through the ionosphere, if the frequency of the electromagnetic waves is low enough, they will be reflected by the ionosphere. If the frequency of the electromagnetic waves is high enough, they will penetrate the ionosphere. Therefore, the electron density can characterize the propagation characteristics of the ionosphere.

[0003] In the existing technology, ISR (Incoherent Scatter Radar) is used to transmit high-power radio waves to the ionosphere, which can achieve beam switching in a short time, and through large-caliber ground radar, partial coherent Thomson scattering is received electronically. After complex signal processing, a variety of ionospheric parameters can be obtained, including electron density, ion composition, plasma temperature and line-of-sight speed, etc., with high temporal and spatial resolution. The literature shows (Yue, X., Wan, W., Ning, B., Jin, L., Ding, F., Zhao, B., et al. (2022). Development of the Sanya Incoherent Scatter Radar and Preliminary Results. Journal of Geophysical Research: Space Physics, 127 (8), e2022JA030451. https: / / doi.org / 10.1029 / 2022JA030451 ), a new modular active digital phased array was built in Sanya (18.3°N, 109.6°E) in China's low latitude. The all-solid-state transmitting and digital receiving ISR signals were used. The Sanya Incoherent Scattering Radar (SYISR) achieved good experimental results for ionospheric parameter detection. The target area calculated by this method is as follows Figure 2 As shown in the red dashed rectangle, the radar detection beam formed by SYISR is as follows Figure 3As shown. If the above method is used, it is not enough to rely solely on the SYISR beam to construct a three-dimensional electron density distribution with high spatial resolution over the target area, because SYISR is scattered upward from the ground radar station, and there is a lack of measured data in other areas that are not covered by the beam. As the radar beam diverges, this problem is particularly obvious at low altitudes. In addition, many scholars also use traditional data assimilation methods to assimilate observations into empirical models, because the empirical model itself has the disadvantage of insufficient accuracy, which will inevitably reduce the accuracy of regional electron density. Summary of the invention

[0004] The present invention proposes a method for calculating the three-dimensional electron density of the ionosphere based on a radar detection area. This method overcomes the disadvantage of insufficient spatial resolution of ISR detection in the background technology and can achieve the electron density distribution with any spatial resolution in the acquisition area.

[0005] The technical solution of the present invention is a method for calculating the three-dimensional electron density of the ionosphere based on the radar detection area. The flow chart is as follows: Figure 1 The specific steps include:

[0006] Step 1: Use ISR to obtain the electron density parameters and geographic coordinates of the detected points in the ISR detection area, which are collectively recorded as ISR observation values.

[0007] Step 2: Use the IRI2020 model to obtain the electron density parameters and geographic coordinates of the detected points in the ISR detection area described in step 1, which are collectively recorded as IRI empirical values.

[0008] Step 3: Train the neural network. The training samples used are: input IRI empirical values ​​of detected points within the ISR detection area, and output ISR observation values ​​corresponding to geographic coordinates.

[0009] Step 4: Use the IRI2020 model to calculate the electron density parameters and geographic coordinates of the undetected points in the ISR detection area, that is, obtain their IRI empirical values, input them into the trained neural network, obtain the ISR observation values ​​of the undetected points in the ISR detection area, and record these undetected points as detected points.

[0010] Repeat steps 3 and 4 until the number of detected points meets the set threshold requirement.

[0011] The ISR observation value of the detected point in the ISR detection area obtained represents the three-dimensional electron density of the ionosphere in the detection area.

[0012] Furthermore, the above neural network uses a fully connected neural network or a feedforward neural network.

[0013] The beneficial effects of the present invention are as follows: the present invention uses IRI-2020 model data and ISR detection data to train a neural network, and uses the neural network to calculate relatively accurate electron density parameters of undetected points in the ISR detection area. Through multiple rounds of neural network iterations, a three-dimensional electron density distribution with arbitrary spatial resolution can be constructed. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the principle flow chart of the present invention;

[0015] Figures 2 to 6 It is a schematic diagram of the simulation experiment results. Specific implementation plan

[0016] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0017] Figure 1 It is a schematic diagram of the principle flow chart of the present invention.

[0018] Figures 2 to 6 It is a schematic diagram of the simulation experiment results. Figure 2 The target area shown is the area where the present invention will calculate the three-dimensional electron density of the ionosphere, and the ISR is located in Sanya (18.3°N, 109.6°E) at low latitudes in China. In order to conduct comparative experiments, a digital altimeter of the Fuk Station is deployed in the target area.

[0019] First, the electron density parameters and geographic coordinates of the detected points in the ISR detection area are obtained and recorded as ISR observation values. Figure 3 shown. Figure 3 The three-dimensional coordinate axes represent longitude, latitude and altitude respectively. The values ​​of the electron density parameters of the detected points in the figure are represented by different colors. The color bar is marked in the figure. Figures 4 to 6 The meanings of the coordinate axes and color bars are the same as Figure 3 same.

[0020] Figure 4 It is the ISR observation value of the undetected point in the ISR detection area calculated after the first execution of step 3 and step 4 of the present invention. At this time, the longitude and latitude resolution between the undetected points is 1 degree. At the same time, the ISR observation value of the undetected point obtained by the digital altimeter of the Fu Ke station is used to verify the accuracy of the ISR observation value of the undetected point obtained by the present invention, and the error between the two is less than 5%.

[0021] Figure 5 Yes Figure 4 After the undetected points are recorded as detected points, the ISR observation values ​​of all detected points are obtained.

[0022] Figure 6 After the steps 3 and 4 of the present invention are executed for the second time, the undetected points are recorded as detected points, and the ISR observation values ​​of all detected points are obtained. At this time, the longitude and latitude resolutions between the detected points all reach 0.2 degrees.

Claims

1. A method for calculating the three-dimensional electron density of the ionosphere based on the radar detection area, including: Step 1: Using ISR, obtain the electron density parameters and geographic coordinates of the detected points within the ISR detection area, which are collectively recorded as ISR observation values. ISR refers to incoherent scattering radar; It is characterized in that Step 2: Use the IRI2020 model to obtain the electron density parameters and geographic coordinates of the detected points in the ISR detection area described in step 1, which are collectively recorded as IRI empirical values; Step 3: Train the neural network. The training samples used are: input IRI empirical values ​​of the detected points in the ISR detection area, and output ISR observation values ​​corresponding to the geographic coordinates; Step 4: Use the IRI2020 model to calculate the electron density parameters and geographic coordinates of the undetected points in the ISR detection area, that is, obtain their IRI empirical values, input them into the trained neural network, obtain the ISR observation values ​​of the undetected points in the ISR detection area, and record these undetected points as detected points; Repeat steps 3 and 4 until the number of detected points meets the set threshold requirement.

2. The method for calculating the three-dimensional electron density of the ionosphere based on the radar detection area according to claim 1, characterized in that: The neural network is a fully connected neural network.

3. The method for calculating the three-dimensional electron density of the ionosphere based on the radar detection area according to claim 1, characterized in that: The neural network is a feedforward neural network.

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