CN110288117B - A regional reconstruction method for critical frequency of ionospheric parameters - Google Patents

Abstract

Translated fromChinese

本发明公开了一种电离层参数临界频率的区域重构方法，它包括如下步骤，步骤A：读取探测站点的经纬度，实时探测的电离层参数foF2以及探测时间；步骤B：选择电离层重构的区域，确定网格点经、纬度；步骤C：通过输入的时间，利用中国参考电离层计算这些网格点上的电离层参数foF2；步骤D：利用Kalman滤波同化方法重构电离层特性参数foF2。有益效果在于：本发明引入了实时的探测数据，在中国参考电离层的基础上进行了融合修正，预报结果的准确度更高，为短波通信选频提供更可靠的数据支撑。本发明基于Kalman滤波的电离层临界频率foF2的区域重构方法，基于实时探测的电离层参数foF2，能够实现更加准确的区域重构预报。The invention discloses a regional reconstruction method for the critical frequency of ionospheric parameters, which comprises the following steps: step A: reading the longitude and latitude of the detection site, real-time detection of the ionospheric parameter foF2 and detection time; step B: selecting the ionospheric weight Determine the longitude and latitude of grid points; Step C: Calculate the ionospheric parameter foF2 on these grid points by using the input time using the Chinese reference ionosphere; Step D: Use the Kalman filter assimilation method to reconstruct the ionospheric characteristics Parameter foF2. The beneficial effects are as follows: the present invention introduces real-time detection data, and performs fusion correction on the basis of the Chinese reference ionosphere, so that the accuracy of prediction results is higher, and more reliable data support is provided for frequency selection of short-wave communication. The present invention is based on the regional reconstruction method of the ionospheric critical frequency foF2 of Kalman filtering, and based on the ionospheric parameter foF2 detected in real time, and can realize more accurate regional reconstruction prediction.

Description

Regional reconstruction method for critical frequency of ionosphere parameters

Technical Field

The invention belongs to a calculation method of ionospheric characteristics, and particularly relates to a regional reconstruction method of ionospheric parameter critical frequency.

Background

The method is based on detection data of dozens of sites, obtains a statistical model of ionosphere change through statistical analysis, and predicts the ionosphere characteristic parameters according to the number of solar black particles. The model has the defects that only the monthly median can be predicted and counted, and the accuracy of the real-time forecasting result is low.

Disclosure of Invention

The invention aims to provide a regional reconstruction method of ionosphere parameter critical frequency, which overcomes the defect that the reference ionosphere in China can only predict the monthly median and realizes more accurate regional reconstruction prediction.

The technical scheme of the invention is as follows: a regional reconstruction method of critical frequency of ionospheric parameters comprises the following steps,

step A: reading the longitude and latitude of a detection station, and detecting the critical frequency foF2 and the detection time of an F2 layer in real time;

and B: selecting an ionosphere reconstruction region, and determining grid point longitude and latitude;

and C: calculating the critical frequency foF2 of the F2 layer on the grid points by using the Chinese reference ionosphere according to the input time;

step D: f2 layer critical frequency foF2 is reconstructed by using a Kalman filtering assimilation method.

The unit of longitude and latitude in the step A is 'degree', and the detection time comprises year, month, day and Beijing.

The area in the step B is a rectangular area, and comprises minimum and maximum longitudes and latitudes, the grid interval size, and the unit of the maximum/minimum longitudes and latitudes and the grid interval size is 'degree'.

The step B comprises the following steps:

step B1: computing grid point latitude vector λ_i,λ_minAnd λ_maxRespectively representing a minimum latitude and a maximum latitude, lambda_dIndicating the size of the grid intervals in the latitudinal direction, λ_nThe number of grid points in the latitudinal direction,

λ_n＝int[(λ_max-λ_min)/λ_d]+1

λ_i＝(λ_min,λ_min+λ_d,λ_min+2×λ_d,……,λ_min+i×λ_d,……,λ_min+λ_n×λ_d,λ_max)

step B2: computing a grid point longitude vector θ_i，θ_minAnd theta_maxRespectively representing the minimum and maximum longitudes, theta_dRepresenting the size of the grid intervals in the longitudinal direction, theta_mThe number of grid points in the longitudinal direction;

θ_m＝int[(θ_max-θ_min)/θ_d]+1

θ_i＝(θ_min,θ_min+θ_d,θ_min+2×θ_d,θ_min+i×θ_d,……,θ_min+θ_m×θ_d,θ_max)

step B3: generating a grid point longitude and latitude matrix of the reconstruction area;

step C, judging whether the time input in the Chinese reference ionosphere is world time or Beijing time, if the time is world time, converting the time with the Beijing time, wherein the conversion formula is BT (Beijing time) UT (world time) + 8; if it is Beijing, no conversion is performed.

The step D includes the steps of,

step D1: computing a background field error covariance matrix P^bThe method refers to the covariance of the error between the monthly mean value result of the position of the detection point calculated by the reference ionosphere in China and the actual detection data;

step D2: calculating a correlation matrix of a background field, calculating the distance of parameters such as latitude, longitude and foF2 of each grid point by using Euclidean distance, calculating a correlation matrix R by using a Gaussian correlation function, wherein three variables in a calculation formula of the Euclidean distance are respectively latitude, longitude and F2 layer critical frequency foF2, the F2 layer critical frequency foF2 is obtained by calculation of a Chinese reference ionosphere, the longitude and the latitude of the grid points are obtained by calculation in the step B,

in the formula (f)_aAnd f_bCritical frequency foF2 of F2 layer corresponding to different grid points a and b respectively, and the unit is MHz, lambda_a，λ_bLatitude, theta, corresponding to different grid points a and b, respectively_a，θ_bRespectively corresponding longitudes of different grid points a and b, wherein l is a characteristic scale and is selected to be 0.01;

step D3: calculating a gain matrix K;

K＝P^bH^T(HP^bH^T+R)^-1

wherein H is an observation operator,

h ═ E, i.e. H (i, j) ═ 1

Step D4: predicting grid point F2 layer critical frequency foF 2;

x^a＝x^b+K(x^°-Hx^b)

wherein x is^aTo predict the result, x^bTo reference ionospheric calculation results, x^°Is a real-time detection result.

The invention has the beneficial effects that: the invention introduces real-time detection data, performs fusion correction on the basis of the Chinese reference ionosphere, has higher accuracy of a forecast result, and provides more reliable data support for frequency selection of short-wave communication. The method for reconstructing the region of the F2 layer critical frequency foF2 based on Kalman filtering can realize more accurate region reconstruction forecast based on the F2 layer critical frequency foF2 detected in real time.

Detailed Description

The present invention will be described in further detail with reference to specific examples.

The regional reconstruction method of the critical frequency of the ionosphere parameters takes the forecast result of the Chinese reference ionosphere as background field data, takes real-time detection data as observation field data, and fuses the background field data and the observation field data by utilizing a Kalman filtering assimilation method, thereby realizing the regional reconstruction of the real-time ionosphere characteristic parameters. The method comprises the following specific steps:

step A: and reading the longitude and latitude of the detection station, and detecting the critical frequency foF2 of the F2 layer and the detection time in real time. The unit of longitude and latitude in the invention is 'degree', and the detection time comprises year, month, day and Beijing.

And B: and selecting a region of ionosphere reconstruction, and determining the longitude and latitude of the grid points. The area in the invention is a rectangular area, comprising minimum and maximum latitude and longitude and grid interval size. The units of the maximum/minimum latitude/longitude and the grid interval size are "degrees".

Further, step B is specifically described as follows:

step B1: computing grid point latitude vector λ_i,λ_minAnd λ_maxRespectively representing a minimum latitude and a maximum latitude, lambda_dRepresenting the size of the grid spacing in the latitudinal direction,λ_nthe number of grid points in the latitudinal direction,

λ_n＝int[(λ_max-λ_min)/λ_d]+1

λ_i＝(λ_min,λ_min+λ_d,λ_min+2×λ_d,……,λ_min+i×λ_d,……,λ_min+λ_n×λ_d,λ_max)

step B2: computing a grid point longitude vector θ_i，θ_minAnd theta_maxRespectively representing the minimum and maximum longitudes, theta_dRepresenting the size of the grid intervals in the longitudinal direction, theta_mThe number of grid points in the longitudinal direction.

θ_m＝int[(θ_max-θ_min)/θ_d]+1

θ_i＝(θ_min,θ_min+θ_d,θ_min+2×θ_d,θ_min+i×θ_d,……,θ_min+θ_m×θ_d,θ_max)

Step B3: and generating a grid point longitude and latitude matrix of the reconstruction area.

And C: the ionospheric parameters foF2 at these grid points are calculated using the chinese reference ionosphere by the time of entry. In the present invention, it is to be noted that, when the time input in the chinese reference ionosphere is world time or beijing, if it is world time, it needs to be converted to beijing time, and the conversion formula is BT (beijing time) ═ UT (world time) + 8; if it is Beijing, the conversion is not performed.

Step D: f2 layer critical frequency foF2 is reconstructed by using a Kalman filtering assimilation method.

Further, step B is specifically described as follows:

step D1: computing a background field error covariance matrix P^bMainly refers to Chinese reference ionosphere calculation detection pointsThe covariance of the monthly value results of the location with the actual probe data error.

Step D2: and calculating a correlation matrix of the background field, wherein the Euclidean distance is used for calculating the distance of parameters such as longitude and latitude, foF2 and the like of each grid point, and a Gaussian correlation function is used for calculating the correlation matrix R. There are three variables in the formula for calculating the euclidean distance, namely latitude, longitude and critical frequency foF2 of F2 layer, where the critical frequency foF2 of F2 layer is obtained by calculation of the chinese reference ionosphere, and the longitude and latitude of the grid point are obtained by calculation of step B.

In the formula (f)_aAnd f_bfoF2 for different grid points a and b, respectively, in MHz.

λ_a，λ_bRespectively corresponding latitudes of different grid points a and b;

θ_a，θ_blongitudes corresponding to different grid points a and b respectively;

l is a characteristic scale, here chosen to be 0.01.

Step D3: calculating a gain matrix K

K＝P^bH^T(HP^bH^T+R)^-1

Where H is the observation operator.

H ═ E, i.e. H (i, j) ═ 1

Step D4: grid point ionospheric parameters are predicted foF 2.

x^a＝x^b+K(x^°-Hx^b)

Wherein x is^aTo predict the result, x^bTo reference ionospheric calculation results, x^°Is a real-time detection result.

In summary, the present invention provides a method for reconstructing the region of the F2 layer critical frequency foF2 based on the Kalman filter assimilation technique. The method has the greatest advantage that the ionosphere parameters foF2 can be reconstructed in the designated area based on the Chinese reference ionosphere model and by combining with the ionosphere data detected in real time, and the method has higher accuracy and is easy to realize in engineering. Experiments prove that the method is suitable for China regions, and regional ionosphere parameters in the global range can be reconstructed if the method adopts an international reference ionosphere model.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (4)

1. A regional reconstruction method of ionospheric parameter critical frequency is characterized in that: which comprises the following steps of,

step A: reading the longitude and latitude of a detection station, and detecting the critical frequency foF2 and the detection time of an F2 layer in real time;

and B: selecting an ionosphere reconstruction region, and determining grid point longitude and latitude;

and C: calculating the ionospheric parameters foF2 at the grid points by using the Chinese reference ionosphere according to the input time;

step D: reconstructing critical frequency foF2 of an F2 layer by using a Kalman filtering assimilation method;

the step B comprises the following steps:

step B1: computing grid point latitude vector λ_i,λ_minAnd λ_maxRespectively representing a minimum latitude and a maximum latitude, lambda_dIndicating the size of the grid intervals in the latitudinal direction, λ_nThe number of grid points in the latitudinal direction,

λ_n＝int[(λ_max-λ_min)/λ_d]+1

λ_i＝(λ_min,λ_min+λ_d,λ_min+2×λ_d,……,λ_min+i×λ_d,……,λ_min+λ_n×λ_d,λ_max)

step B2: computing a grid point longitude vector θ_i，θ_minAnd theta_maxRespectively representing the minimum and maximum longitudes, [ theta ]_dRepresenting the size of the grid intervals in the longitudinal direction, theta_mThe number of grid points in the longitudinal direction;

θ_m＝int[(θ_max-θ_min)/θ_d]+1

θ_i＝(θ_min,θ_min+θ_d,θ_min+2×θ_d,θ_min+i×θ_d,……,θ_min+θ_m×θ_d,θ_max)

step B3: generating a grid point longitude and latitude matrix of the reconstruction area;

the step D includes the steps of,

step D1: computing a background field error covariance matrix P^bThe method refers to the covariance of the monthly value result of the position of the detection point calculated by the Chinese reference ionosphere and the error of actual detection data;

step D2: calculating a correlation matrix of a background field, calculating longitude and latitude of each grid point and a distance of F2 layer critical frequency foF2 by using Euclidean distance, and calculating a correlation matrix R by using a Gaussian correlation function, wherein three variables in a calculation formula of the Euclidean distance are respectively latitude, longitude and F2 layer critical frequency foF2, the F2 layer critical frequency foF2 is obtained by calculation of a Chinese reference ionosphere, the longitude and latitude of the grid points are obtained by calculation of the step B,

in the formula (f)_aAnd f_bfoF2 corresponding to different grid points a and b respectively, and the unit is MHz, lambda_a，λ_bLatitude, theta, corresponding to different grid points a and b, respectively_a，θ_bRespectively corresponding longitudes of different grid points a and b, wherein l is a characteristic scale and is selected to be 0.01;

step D3: calculating a gain matrix K;

K＝P^bH^T(HP^bH^T+R)^-1

wherein H is an observation operator,

h ═ E, i.e. H (i, j) ═ 1

Step D4: predicting grid point F2 layer critical frequency foF 2;

x^a＝x^b+K(x^o-Hx^b)

wherein x is^aTo predict the result, x^bTo reference ionospheric calculation results, x^oIs a real-time detection result.

2. A method for regional reconstruction of critical frequencies of ionospheric parameters as defined in claim 1, wherein: the unit of longitude and latitude in the step A is 'degree', and the detection time comprises year, month, day and Beijing.

3. A method for regional reconstruction of critical frequencies of ionospheric parameters as defined in claim 1, wherein: the area in the step B is a rectangular area, and comprises minimum and maximum longitudes and latitudes, the grid interval size, and the unit of the maximum/minimum longitudes and latitudes and the grid interval size is 'degree'.

4. A method for regional reconstruction of critical frequencies of ionospheric parameters as defined in claim 1, wherein: step C, judging whether the time input in the Chinese reference ionosphere is world time or Beijing time, if so, converting the time with the Beijing time, wherein the conversion formula is BT (Beijing time) UT (world time) + 8; if it is Beijing, no conversion is performed.