CN116232453B

CN116232453B - Satellite terahertz communication channel atmosphere transmission loss calculation method

Info

Publication number: CN116232453B
Application number: CN202310268127.0A
Authority: CN
Inventors: 何元智; 祃宸升
Original assignee: Institute of Systems Engineering of PLA Academy of Military Sciences
Current assignee: Institute of Systems Engineering of PLA Academy of Military Sciences
Priority date: 2023-03-20
Filing date: 2023-03-20
Publication date: 2023-08-15
Anticipated expiration: 2043-03-20
Also published as: CN116232453A

Abstract

The invention discloses a satellite terahertz communication channel atmospheric transmission loss calculation method, which comprises the following steps: discrete data information of atmospheric parameters and information of atmosphere gas molecule composition in the range of a satellite terahertz communication link are acquired; calculating to obtain layered height range information and layered junction height range information; constructing a curvature dynamic optimal planning model and a dynamic linear system optimal model, and continuously carrying out altitude atmosphere parameter information within the range of a satellite terahertz communication link; obtaining an atmospheric attenuation coefficient of the terahertz communication frequency band by using an atmospheric attenuation coefficient calculation model of the terahertz communication frequency band; and obtaining the atmospheric transmission loss value of the terahertz communication channel of the satellite by using the terahertz channel atmospheric transmission loss calculation model. The method solves the problems of difficult measurement of meteorological data and original data loss, can predict the missing data according to a small amount of effective data, calculates the atmosphere transmission loss of the terahertz channel, and has the advantages of high speed, high precision and stable calculation.

Description

Satellite terahertz communication channel atmosphere transmission loss calculation method

Technical Field

The invention belongs to the field of terahertz communication, and particularly relates to a satellite terahertz communication channel atmospheric transmission loss calculation method.

Background

In recent years, terahertz communication technology has been rapidly developed, which will play a great role in realizing space-large-capacity communication in the aerospace field as a new generation communication technology. In the future, data communication between satellites or between satellites and the ground is expected to be realized by relying on terahertz communication technology. Although the terahertz frequency band can provide a larger bandwidth and a higher transmission capacity, the electromagnetic wave of the frequency band has larger attenuation when propagating in the atmosphere, and the attenuation is particularly serious when the water molecules in the air are more, and when the star-earth terahertz communication link budget is carried out, the atmospheric transmission loss needs to be considered seriously, so how to calculate the transmission loss of the terahertz wave in the atmosphere is a very important problem.

In order to accurately calculate the atmospheric transmission absorption attenuation of the terahertz waves, relevant air pressure, water vapor density and temperature parameters along a propagation path are required to be obtained. Because of the difficulty in atmospheric observation, the problems of small vertical expansion height range, uneven data distribution of different heights and the like of measured data exist. The China Different Heights Annually THz Wave Atmospheric Absorption Calculation document proposes a method for calculating the atmospheric transmission absorption attenuation of terahertz waves, which is based on the atmospheric layering theory and is used for layering the atmosphere. And calculating the propagation loss of each layer of atmosphere by averaging the meteorological parameters of each layer of atmosphere, and finally, superposing the propagation loss of each layer of atmosphere to obtain the total atmospheric absorption loss of the satellite-ground link.

The existing method for calculating the atmospheric transmission absorption attenuation of the terahertz waves is low in accuracy, cannot meet actual requirements, and is not considered in the connection of all layers of the atmosphere, so that the calculated atmospheric absorption attenuation loss is low.

Disclosure of Invention

Aiming at the problems that in the estimation process of terahertz wave atmospheric transmission absorption attenuation in satellite communication link estimation, the atmospheric data has small vertical expansion height range and uneven data distribution at different heights, so that the atmospheric absorption attenuation loss is low, the invention discloses a satellite terahertz communication channel atmospheric transmission loss calculation method.

The invention discloses a satellite terahertz communication channel atmospheric transmission loss calculation method, which comprises the following steps:

s1, acquiring discrete data information of atmospheric parameters and composition information of atmospheric gas molecules in a satellite terahertz communication link range; the discrete data information of the atmospheric parameters comprises atmospheric pressure, water vapor density and atmospheric temperature at discrete height values;

S2, processing discrete data information of the atmospheric parameters by using an atmospheric equal-quality layering model to obtain layering height range information and layering junction height range information;

s3, constructing a curvature dynamic optimal planning model by utilizing the height range information of the layered junction and the discrete data information of the atmospheric layer parameters; processing the height range information of the layered junction by using the curvature dynamic optimal planning model to obtain an atmosphere parameter set of continuous height of the layered junction;

s4, constructing a dynamic linear system optimization model by utilizing the layering height range information and the discrete data information of the atmospheric layer parameters; processing the layered height range information by using the dynamic linear system optimization model to obtain an atmosphere parameter set of continuous heights of the layered height range;

s5, integrating the atmosphere parameter set with the continuous height in the layered height range and the atmosphere parameter set with the continuous height at the layered connection position to obtain the atmosphere parameter information with the continuous height in the satellite terahertz communication link range;

s6, processing continuous altitude atmosphere parameter information in the range of the terahertz communication link of the satellite by using an atmospheric attenuation coefficient calculation model of the terahertz communication frequency band to obtain an atmospheric attenuation coefficient of the terahertz communication frequency band; the atmospheric attenuation coefficient calculation model of the terahertz communication frequency band comprises a terahertz dry air absorption coefficient calculation model and a terahertz communication frequency band water vapor absorption coefficient calculation model;

And S7, processing the atmospheric attenuation coefficient of the terahertz communication frequency band and the transmission distance of the satellite communication link by using a terahertz channel atmospheric transmission loss calculation model to obtain the satellite terahertz communication channel atmospheric transmission loss value.

The processing of the discrete data information of the atmospheric parameters by using the atmospheric equal-quality layering model to obtain layering height range information and layering junction height range information comprises the following steps:

s21, determining effective height information of each type of atmosphere parameters according to the atmosphere gas molecular composition information;

s22, calculating the layering thickness information of each type of atmosphere parameters within the effective height of each type of atmosphere parameters by using a layering thickness model; the layering thickness model has the expression:

wherein n is a layering sequence number, the value of which increases from the ground upwards, delta _n Thickness information for the nth layer;

s23, according to the effective height information and the layering thickness information of each type of atmosphere parameters, from the 1 st layer to the highest layer, carrying out superposition processing on the layer thickness information to obtain initial layering height range information of each type of atmosphere parameters;

s24, extracting the junction point height information of two adjacent layers from the initial layered height range information of each type of atmosphere parameters;

S25, regarding each type of atmosphere parameter, taking the junction point height information as a center, and taking the center extending upwards and downwards by the same preset height value as a layered connection position; the height value range corresponding to the same preset height value extending upwards and downwards from the center is used as the height range information of the layered connection part; the preset height value is obtained by multiplying the sum of two adjacent layering heights of the center by a set proportion value;

s26, deleting the height range information of the layered connection part of each type of atmosphere parameters from the initial layered height range information to obtain layered height range information of each type of atmosphere parameters.

Constructing a curvature dynamic optimal planning model by utilizing the height range information of the layered connection part and the discrete data information of the atmospheric layer parameters; processing the height range information of the layered junction by using a curvature dynamic optimal planning model to obtain an atmosphere parameter set of continuous heights of the layered junction, wherein the method comprises the following steps:

s31, for each type of atmosphere parameters, extracting discrete data information of the layered connection positions from the discrete data information of the atmosphere parameters according to the height range information of the layered connection positions; the discrete data information of the layered connection comprises discrete height information and discrete atmosphere parameter information of the layered connection;

S32, processing discrete data information of the layered junction by using a curvature statistical analysis method and a first iteration solution model, and constructing to obtain a curvature dynamic optimal planning model;

s33, processing discrete height information of the layered junction by using the curvature dynamic optimal planning model to obtain an atmosphere parameter set of continuous height of the layered junction.

The discrete data information at the layered junction is processed by using a curvature statistical analysis method and a first iteration solution model, and a curvature dynamic optimal planning model is constructed and obtained, and the method comprises the following steps:

s321, integrating discrete height information of the layered connection part, and establishing a discrete height set of the layered connection part;

s322, integrating the discrete atmosphere parameter information of the layered junction, and respectively establishing a corresponding discrete atmosphere parameter information set of the layered junction for each type of atmosphere parameter;

s323, for each type of atmosphere parameters, respectively taking the discrete height set of the layered junction as an independent variable, taking the discrete atmosphere parameter information set of the layered junction as a dependent variable, and establishing a corresponding mapping function of the layered junction by utilizing the corresponding relation between the independent variable and the dependent variable;

S324, uniformly sampling the discrete height sets at the layered connection positions according to a first preset height interval to obtain first discrete height sets;

s325, calculating the curvature value of the hierarchical junction mapping function at the corresponding height value according to the first discrete height set to obtain a hierarchical junction curvature value set;

s326, carrying out statistical analysis processing on the curvature value set of the layered junction to obtain a first curvature statistical characteristic; the first curvature statistical feature comprises a mean value and a variance of a curvature value set at a layered joint;

s327, determining the polynomial order and the initial polynomial coefficient of a curvature dynamic optimal planning model according to the first curvature statistical characteristics;

s328, processing discrete data information at the joint of the initial polynomial and the layering by adopting a first iteration solving model, and calculating to obtain a high-order polynomial coefficient;

and S329, integrating the initial polynomial and the higher order polynomial to obtain a curvature dynamic optimal planning model.

S321, integrating the discrete height information of the layered connection part, and establishing a discrete height set of the layered connection part, wherein the height information set of the mth layered connection part is expressed as H _m ＝{h _m1 ,h _m2 ,...,h _ms -wherein s is the number of discrete heights per layer;

s322, integrating the discrete atmosphere parameter information of the layered junction, and respectively establishing a corresponding discrete atmosphere parameter information set of the layered junction for each type of atmosphere parameter; three atmosphere parameter information sets of discrete atmospheric pressure, water vapor density and atmospheric temperature at the mth layered connection part are respectively defined as P _m 、ρ _m 、T _m The expression is:

P _m ＝{p _m1 ,p _m2 ,...,p _ms }、ρ _m ＝{ρ _m1 ,ρ _m2 ,...,ρ _ms }、T _m ＝{t _m1 ,t _m2 ,...,t _ms p is }, where _mi 、ρ _mi 、t _mi I-th data representing discrete sets of parameter information for atmospheric pressure, vapor density, atmospheric temperature, i=1, 2, …, s, respectively, at the mth layered junction;

s323, for each type of atmosphere parameters, respectively taking the discrete height set of the layered junction as an independent variable, taking the discrete atmosphere parameter information set of the layered junction as a dependent variable, and establishing a corresponding mapping function of the layered junction by utilizing the corresponding relation between the independent variable and the dependent variable; for the mth layered joint, with H _m As independent variables, respectively by P _m 、ρ _m 、T _m Establishing a layered junction mapping function of atmospheric pressure, water vapor density and atmospheric temperature as dependent variables;

s326, carrying out statistical analysis processing on the curvature value set of the layered junction to obtain a first curvature statistical characteristic; the first curvature statistical feature comprises a mean value mu of curvature value sets at layered joints ₁ Sum of variances delta ₁ ；

S327, for each type of atmosphere parameter, setting a first range threshold value a ₁ And a ₂ Difference of the other delta ₁ And mean mu ₁ Ratio k of (2) ₁ To make a discrimination when k ₁ ≤a ₁ When the polynomial order of the dynamic optimal planning model is determined to be c ₁ The method comprises the steps of carrying out a first treatment on the surface of the When a is ₁ ＜k ₁ ≤a ₂ When the polynomial order of the dynamic optimal planning model is determined to be c ₂ The method comprises the steps of carrying out a first treatment on the surface of the When a is ₂ ＜k ₁ When the polynomial order of the dynamic optimal planning model is determined to be c ₃ The method comprises the steps of carrying out a first treatment on the surface of the When k is ₁ ≤a ₂ Determining initial polynomial coefficients of a dynamic optimal planning modelIs d ₁ The method comprises the steps of carrying out a first treatment on the surface of the When a is ₂ ＜k ₁ When the initial polynomial coefficient of the dynamic optimal planning model is determined to be d ₂ The method comprises the steps of carrying out a first treatment on the surface of the The polynomial order satisfies c ₁ ＜c ₂ ＜c ₃ The initial polynomial coefficient satisfies d ₁ ＜d ₂ ；

S328, processing discrete data information at the connection position of the initial polynomial and the layering by adopting a first iteration solving model, and calculating to obtain a high-order polynomial coefficient, wherein the method comprises the following steps:

s3281, carrying out iterative processing on the discrete data information of the initial polynomial and the layered junction to obtain an expression of a high-order polynomial; the iterative process has the expression:

wherein, at the mth layered junction, the orders of polynomials determined for the atmospheric pressure, the vapor density and the atmospheric temperature are s1, s2 and s3 respectively, and the initial polynomials determined are G respectively _Pm0 (h _m )，G _ρm0 (h _m )，G _Tm0 (h _m )，h _m For the continuous height value of the mth layered junction, G _Pm0 (h _m )、G _ρm0 (h _m )、G _Tm0 (h _m ) An initial polynomial of atmospheric pressure, vapor density and atmospheric temperature, G, respectively, at the mth layered junction _Pm1 (h _m )、G _ρm1 (h _m )、G _Tm1 (h _m ) 1 st order polynomials of atmospheric pressure, water vapor density and atmospheric temperature at the mth layered junction, G _Pm(k+1) (h _m )、G _ρm(k+1) (h _m )、G _Tm(k+1) (h _m ) A k+1st order polynomial of the atmospheric pressure, the water vapor density and the atmospheric temperature, respectively, (u) _Pmk 、v _Pm(k-1) )、(u _ρmk 、v _ρm(k-1) )、(u _Tmk 、v _Tm(k-1) ) Internal coefficients of the k+1st order polynomial of the atmospheric pressure, vapor density and atmospheric temperature, u _Pm0 、u _ρm0 、u _Tm0 The internal coefficients of the 1 st order polynomials of atmospheric pressure, vapor density and atmospheric temperature, respectively;

S3282, obtaining the internal coefficients of the high-order polynomials through iterative computation according to the expression of the high-order polynomials; the expression of the iterative calculation is as follows:

wherein p is _mi 、ρ _mi 、t _mi The parameter value of the ith discrete atmospheric layer at the mth layered junction, which is the ith discrete height h at the mth layered junction, is the atmospheric pressure, the vapor density and the atmospheric temperature, respectively _mi The corresponding discrete atmospheric layer parameter values;

s3283, according to the expression of the high-order polynomials, calculating to obtain the external coefficients of each polynomial, wherein the calculation expression is as follows:

wherein f _Pmk 、f _ρmk 、f _Tmk The atmospheric pressure and the vapor tightness of the m-th layered connection part are respectivelyExternal coefficients of a kth order polynomial of degree and atmospheric temperature; the external coefficients and the internal coefficients of the higher order polynomial coefficients together constitute the higher order polynomial coefficients; the external coefficients of the initial polynomial are all 1;

and S329, carrying out weighted summation on the initial polynomial and the high-order polynomial by using the external coefficients of the polynomial to obtain a curvature dynamic optimal planning model.

Constructing a dynamic linear system optimization model by using the layering height range information and the discrete data information of the atmospheric layer parameters; and processing the layered height range information by using a dynamic linear system optimization model to obtain an atmosphere parameter set of continuous heights of the layered height range, wherein the method comprises the following steps:

S41, extracting discrete data information of each layering height range from the discrete data information of the atmospheric parameters according to layering height range information of each class of atmospheric parameters; the discrete data information of each layering height range comprises discrete height information of the layering height range and discrete atmosphere parameter information;

s42, processing the discrete data information of the layering height range by using a slope statistical analysis method and a second iteration solution model, and constructing to obtain a dynamic linear system optimization model;

s43, processing the discrete height information of the layered height range by using the dynamic linear system optimization model, and calculating to obtain an atmosphere parameter information set of continuous heights in the layered height range.

The discrete data information of the layering height range is processed by using a slope statistical analysis method and a second iteration solution model, and a dynamic linear system optimization model is constructed and obtained, and the method comprises the following steps:

s421, integrating the discrete height information of the layering height range, and establishing a layering height range discrete height set;

s422, integrating the discrete atmosphere parameter information of the layering height range, and respectively establishing corresponding discrete atmosphere parameter information sets of the layering height range for each type of atmosphere parameter;

S423, for each type of atmosphere parameters, respectively taking the layered height range discrete height set as an independent variable, taking the layered height range discrete atmosphere parameter information set as a dependent variable, and establishing a corresponding layered height range mapping function by utilizing the corresponding relation between the independent variable and the dependent variable;

s424, uniformly sampling the layered height range discrete height set according to a second preset height interval to obtain a second discrete height set;

s425, calculating the slope value of the hierarchical height range mapping function at the corresponding height value according to the second discrete height set to obtain the hierarchical height range slope value set;

s426, carrying out statistical analysis processing on the layered height range slope value set to obtain a first slope statistical characteristic; the first slope statistics comprise means and variances of a set of layered height range slope values;

s427, determining the polynomial order of a dynamic linear system optimization model according to the first slope statistical characteristics;

s428, processing discrete data information of the layering height range by adopting a second iteration solving model, and calculating to obtain a high-order polynomial coefficient;

S429, integrating the high-order polynomials to obtain a dynamic linear system optimization model.

s421, integrating the discrete height information of the layering height range, and establishing a layering height range discrete height set, wherein the nth layering height range discrete height information set is expressed as H _n ＝{h _n1 ,h _n2 ,...,h _ns And (b) wherein h _ni I=1, 2, i..4, s4 is the number of discrete heights per layer;

s422, integrating the discrete atmosphere parameter information of the layering height range, and respectively establishing corresponding discrete atmosphere parameter information sets of the layering height range for each type of atmosphere parameter; the information sets of three atmosphere parameters including discrete atmospheric pressure, water vapor density and atmospheric temperature in the nth layering height range are respectively defined as P _n 、ρ _n 、T _n The expression is:

P _n ＝{p _n1 ,p _n2 ,...,p _ns }、ρ _n ＝{ρ _n1 ,ρ _n2 ,...,ρ _ns }、T _n ＝{t _n1 ,t _n2 ,...,t _ns }，

wherein p is _ni 、ρ _ni 、t _ni I-th data representing a set of parameter information for discrete atmospheric pressure, vapor density, atmospheric temperature for an n-th range of stratification heights, i=1, 2, …, s4;

S423, for each type of atmosphere parameters, respectively taking the layered height range discrete height set as an independent variable, taking the layered height range discrete atmosphere parameter information set as a dependent variable, and establishing a corresponding layered height range mapping function by utilizing the corresponding relation between the independent variable and the dependent variable; for the nth hierarchical height range, with H _n As independent variables, respectively by P _n 、ρ _n 、T _n Establishing a layering height range mapping function of atmospheric pressure, water vapor density and atmospheric temperature as dependent variables;

s425, calculating the slope value of the hierarchical height range mapping function at the corresponding height value according to the second discrete height set to obtain a hierarchical height range slope value set;

s426, carrying out statistical analysis processing on the layered height range slope value set to obtain a first slope statistical characteristic; the first slope statistics include a mean μ of a set of layered height range curvature values ₂ Sum of variances delta ₂ ；

S427, for each type of atmosphere parameter, setting a second range threshold value a ₃ Difference of the other delta ₂ And mean mu ₂ Ratio k of (2) ₂ To make a discrimination when k ₂ ≤a ₃ Determining the polynomial order of the dynamic linear system optimization model to be 3; when a is ₃ ＜k ₂ Determining the polynomial order of the dynamic linear system optimization model to be 5;

s428, processing discrete data information of the layering height range by adopting a second iteration solving model, and calculating to obtain polynomial coefficients, wherein the method comprises the following steps of:

s4281, dividing the layering height range into a plurality of subsections according to the discrete height information of the layering height range; constructing a corresponding sub-segment linear polynomial according to the polynomial order of the determined dynamic linear system optimization model for each sub-segment; all sub-segment linear polynomials constitute the hierarchical polynomial;

s4282, for the layered polynomial, establishing constraint conditions by using a layered height range discrete atmosphere parameter information set and a layered polynomial high-order derivative continuous condition, and establishing boundary conditions by using the condition that the three-order derivative values of the upper and lower boundary values of the discrete height of each boundary subsection of the layered polynomial are the same; solving to obtain coefficients of each sub-segment linear polynomial by using constraint conditions and boundary conditions, wherein the coefficients of all the sub-segment linear polynomials form the coefficients of the layered polynomials;

S429, integrating all the layered polynomials of each type of atmosphere parameters to obtain a dynamic linear system optimization model.

The method for processing the continuous altitude atmosphere parameter information in the range of the terahertz communication link of the satellite by using the atmospheric attenuation coefficient calculation model of the terahertz communication frequency band to obtain the atmospheric attenuation coefficient of the terahertz communication frequency band comprises the following steps:

s61, processing continuous altitude atmosphere parameter information in the satellite terahertz communication link range by using a terahertz dry air absorption coefficient calculation model to obtain terahertz communication frequency with set altitudeAbsorption coefficient gamma of air for section drying _dry The method comprises the steps of carrying out a first treatment on the surface of the The terahertz dry air absorption coefficient calculation model has the expression:

wherein v is terahertz communication frequency, P is atmospheric pressure with set height, e is vapor pressure with set height, and T is atmospheric temperature with set height;

s62, processing continuous altitude atmosphere parameter information in the range of the satellite terahertz communication link by using a terahertz communication frequency band water vapor absorption coefficient calculation model to obtain a terahertz communication frequency band water vapor absorption coefficient gamma with a set altitude _H2O The method comprises the steps of carrying out a first treatment on the surface of the The terahertz communication frequency band water vapor absorption coefficient calculation model has the expression:

Wherein ρ is the set height of the water vapor density;

and S63, summing the terahertz communication frequency band dry air absorption coefficient and the terahertz communication frequency band water vapor absorption coefficient to obtain an atmospheric attenuation coefficient gamma of the terahertz communication frequency band.

The method for processing the atmospheric attenuation coefficient of the terahertz communication frequency band and the transmission distance of the satellite communication link by using the terahertz channel atmospheric transmission loss calculation model to obtain the satellite terahertz communication channel atmospheric transmission loss value comprises the following steps:

s71, determining the effective atmospheric height h according to the transmission distance D of the satellite communication link _e The method comprises the steps of carrying out a first treatment on the surface of the When D < 30km, the effective height h of the atmosphere _e When D is larger than or equal to 30km, the effective atmospheric height h _e Is 30km;

s72, processing the atmospheric attenuation coefficient of the terahertz communication frequency band by using a terahertz channel atmospheric transmission loss calculation model to obtain an atmospheric transmission loss value of a satellite terahertz communication channel; the terahertz channel atmospheric transmission loss calculation model has the expression:

wherein A is _gas And θ (h) is the communication elevation angle of the satellite communication link at h height, and γ (h) is the atmospheric attenuation coefficient of the terahertz communication frequency band at h height.

The invention has the following advantages:

1. the invention discloses a terahertz channel atmospheric transmission loss calculation method, which is used for converting discrete atmospheric data, converting the discrete data into a continuous curve before calculating an attenuation coefficient and obtaining atmospheric parameters with any height, so that the terahertz wave atmospheric transmission absorption attenuation is accurately calculated, and the terahertz wave atmospheric transmission absorption attenuation can be quickly, stably and accurately solved.

2. The method solves the problems of difficult measurement of meteorological data and missing of original data in the calculation of the terahertz channel atmospheric transmission loss, can predict missing data according to a small amount of effective data, calculates the terahertz channel atmospheric transmission loss, and has the advantages of high speed, high precision and stable calculation.

Drawings

Fig. 1 is a flow chart of an implementation of the satellite terahertz communication channel atmospheric transmission loss calculation method in the invention.

Detailed Description

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

The embodiment discloses a satellite terahertz communication channel atmospheric transmission loss calculation method, and fig. 1 is a realization flow of the satellite terahertz communication channel atmospheric transmission loss calculation method, including:

S1, acquiring discrete data information of atmospheric parameters and composition information of atmospheric gas molecules in a satellite terahertz communication link range; the discrete data information of the atmospheric parameters comprises atmospheric pressure, water vapor density and atmospheric temperature at discrete height values; the atmosphere gas molecular composition information comprises composition information of water vapor, oxygen and other gases of the atmosphere. The atmospheric parameters comprise three types of atmospheric pressure, water vapor density and atmospheric temperature;

the discrete data of the atmospheric layer parameters and the information of the atmospheric layer gas molecular group can be acquired from an Internet database. In particular, it may be obtained from a related climate data website.

S4, constructing a dynamic linear system optimization model by utilizing the layering height range information and the discrete data information of the atmospheric layer parameters; processing the layering height range information by using the dynamic linear system optimization model to obtain an atmosphere layer parameter set of continuous heights of the layering height range

s21, determining effective height information of each type of atmosphere parameters according to the atmosphere gas molecular composition information; the step S21 includes: according to the atmospheric gas molecular composition information, determining the effective height information of each type of atmospheric parameters according to three types of atmospheric parameters, namely atmospheric pressure, water vapor density and atmospheric temperature, wherein the effective height of the atmospheric pressure and the atmospheric temperature is 30km, and the effective height of the water vapor density is 16km;

in the step S25, the setting ratio of the sum of the two adjacent layering heights may be 5%; in the step S25, the boundary point between the second layer and the third layer may be taken as the center, and the sum of the heights of the second layer and the third layer is 10km, and then a height range of 0.5km (10 km×5%) up and 0.5km (10 km×5%) down is defined as a layer connection, where the height information of the layer connection is (center height-0.5 km, center height +0.5 km).

In the step S33, discrete height information in the discrete data information of the layered junction may be input to the curvature dynamic optimal planning model to obtain an atmospheric parameter set of continuous height of the layered junction.

s321, integrating the discrete height information of the layered connection part, and establishing a discrete height set of the layered connection part, wherein the height information set of the mth layered connection part is expressed as H _m ＝{h _m1 ,h _m2 ,...,h _ms -wherein s is the discrete number of levels per level, also the number of atmospheric parameters per class of the set of level information at the mth level junction;

S327, for each type of atmosphere parameter, setting a first range threshold value a ₁ And a ₂ Difference of the other delta ₁ And mean mu ₁ Ratio k of (2) ₁ To make a discrimination when k ₁ ≤a ₁ When the polynomial order of the dynamic optimal planning model is determined to be c ₁ The method comprises the steps of carrying out a first treatment on the surface of the When a is ₁ ＜k ₁ ≤a ₂ When the polynomial order of the dynamic optimal planning model is determined to be c ₂ The method comprises the steps of carrying out a first treatment on the surface of the When a is ₂ ＜k ₁ When the polynomial order of the dynamic optimal planning model is determined to be c ₃ The method comprises the steps of carrying out a first treatment on the surface of the When k is ₁ ≤a ₂ When the initial polynomial coefficient of the dynamic optimal planning model is determined to be d ₁ The method comprises the steps of carrying out a first treatment on the surface of the When a is ₂ ＜k ₁ When the initial polynomial coefficient of the dynamic optimal planning model is determined to be d ₂ The method comprises the steps of carrying out a first treatment on the surface of the The polynomial order satisfies c ₁ ＜c ₂ ＜c ₃ The initial polynomial coefficient satisfies d ₁ ＜d ₂ ；

wherein f _Pmk 、f _ρmk 、f _Tmk External coefficients of a kth order polynomial of atmospheric pressure, vapor density and atmospheric temperature at the mth layered junction, respectively; the external coefficients and the internal coefficients of the higher order polynomial coefficients together constitute the higher order polynomial coefficients; the external coefficients of the initial polynomial are all 1;

In the step S43, discrete height information of the layered height range may be input to the dynamic linear system optimization model, so as to obtain an atmospheric parameter information set of continuous heights in the layered height range.

The step S42 includes:

s421, integrating the discrete height information of the layering height range, and establishing a layering height range discrete height set, wherein the nth layering height range discrete height information set is expressed as H _n ＝{h _n1 ,h _n2 ,...,h _ns And (b) wherein h _ni I=1, 2,..s 4, s4 is the number of discrete height per layer, i.e., i=1, 2,..s 4 is the number of atmospheric parameters per class of the height information set for the n-th layer height range;

s424, uniformly sampling the layered height range discrete height set according to a second preset height interval to obtain a second discrete height set; the first preset height interval may be 1km;

S427, for each type of atmosphere parameter, setting a second range threshold value a ₃ Difference of the other delta ₂ And mean mu ₂ Ratio k of (2) ₂ To make a discrimination when k ₂ ≤a ₃ Determining the polynomial order of the dynamic linear system optimization model to be 3; when a is ₃ ＜k ₂ Determining the polynomial order of the dynamic linear system optimization model to be 5; for each type of atmospheric parameters, the set parameter a ₃ May be the same or different.

constructing a corresponding sub-segment linear polynomial for each sub-segment according to the polynomial order of the determined dynamic linear system optimization model; all sub-segment linear polynomials, constituting the hierarchical polynomial, comprise:

for the ith subsection of the nth hierarchy [ h ] _ni ,h _n(i+1) ]When the polynomial order of the dynamic linear system optimization model is 3, the corresponding sub-segment linear polynomial is

S _Pni (h _n )＝a _Pni h _n ³ +b _Pni h _n ² +c _Pni h _n +d _Pni ，

S _ρni (h _n )＝a _ρni h _n ³ +b _ρni h _n ² +c _ρni h _n +d _ρni ，

S _Tni (h _n )＝a _Tni h _n ³ +b _Tni h _n ² +c _Tni h _n +d _Tni ，

Wherein h is _ni H is the lower boundary of the ith subsection of the nth hierarchy _n(i+1) An upper boundary of the ith sub-segment of the nth hierarchy, S _Pni (h _n )、S _ρni (h _n )、S _Tni (h _n ) An nth sub-segment linear polynomial of an nth hierarchy of atmospheric pressure, vapor density, atmospheric temperature, h _n For the nth hierarchical continuous height value, a _Pni 、b _Pni 、c _Pni 、d _Pni An ith sub-section linear polynomial for an nth stratification of atmospheric pressureCoefficient, a _ρni 、b _ρni 、c _ρni 、d _ρni An ith sub-segment linear polynomial coefficient, a, for an nth stratification of water vapor density _Tni 、b _Tni 、c _Tni 、d _Tni An ith sub-segment linear polynomial coefficient for an nth stratification of atmospheric temperature; for the atmospheric pressure, the water vapor density and the atmospheric temperature, the expressions of the corresponding nth layered polynomials are respectively:

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wherein S is _Pn (h _n )、S _ρn (h _n )、S _Tn (h _n ) Respectively expressing the nth layered polynomial, wherein d is the number of nth layered subsections of atmospheric pressure, water vapor density and atmospheric temperature;

when the polynomial order of the dynamic linear system optimization model is 5, the corresponding sub-segment linear polynomial is:

S _Pni (h _n )＝f _Pni h _n ⁵ +g _Pni h _n ⁴ +k _Pni h _n ³ +l _Pni h _n ² +m _Pni h _n +n _Pni ，

S _ρni (h _n )＝f _ρni h _n ⁵ +g _ρni h _n ⁴ +k _ρni h _n ³ +l _ρni h _n ² +m _ρni h _n +n _ρni ，

S _Tni (h _n )＝f _Tni h _n ⁵ +g _Tni h _n ⁴ +k _Tni h _n ³ +l _Tni h _n ² +m _Tni h _n +n _Tni ，

the variable meaning and the composition of the hierarchical polynomial are the same as those of the 3 rd order polynomial, except that the order of the polynomial is extended to 5 th order.

The establishing constraint conditions by using the discrete atmosphere parameter information set of the layered height range and the continuous conditions of the layered polynomial high-order derivatives comprises the following steps:

for the ith subsection of the nth hierarchy [ h ] _ni ,h _n(i+1) ]When the polynomial order of the dynamic linear system optimization model is 3, the corresponding constraint conditions are as follows:

wherein p is _ni 、ρ _ni 、t _ni The i discrete atmospheric layer parameter values of the atmospheric pressure, the water vapor density and the atmospheric temperature at the n-th layered connection are respectively the discrete height h _ni Corresponding discrete atmospheric layer parameter value S _Pn (h _ni +0)、S _ρn (h _ni +0)、S _Tn (h _ni +0) the nth hierarchical polynomial of atmospheric pressure, vapor density and atmospheric temperature, respectively, at h _ni Right continuation value at; s is S _Pn (h _ni -0)、S _ρn (h _ni -0)、S _Tn (h _ni -0) nth of atmospheric pressure, water vapor density and atmospheric temperature respectivelyLayered polynomial at h _ni Left continuation value at; s is S _Pn '(h _ni +0)、S _ρn '(h _ni +0)、S _Tn '(h _ni +0) the nth hierarchical polynomial of atmospheric pressure, vapor density and atmospheric temperature, respectively, at h _ni Right derivative value at; s is S _Pn '(h _ni -0)、S _ρn '(h _ni -0)、S _Tn '(h _ni -0) an nth hierarchical polynomial of atmospheric pressure, vapor density and atmospheric temperature, respectively, at h _ni Left derivative value at; s is S _Pn ”(h _ni +0)、S _ρn ”(h _ni +0)、S _Tn ”(h _ni +0) the nth hierarchical polynomial of atmospheric pressure, vapor density and atmospheric temperature, respectively, at h _ni A second order right derivative value at; s is S _Pn ”(h _ni -0)、S _ρn ”(h _ni -0)、S _Tn ”(h _ni -0) an nth hierarchical polynomial of atmospheric pressure, vapor density and atmospheric temperature, respectively, at h _ni A second order left derivative value at; in the case of the order of 5, the polynomial order is changed to 5 as in the case of the order of 3.

The method for establishing boundary conditions by using the hierarchical polynomial meets the condition that the third-order derivative values of the upper boundary value and the lower boundary value of the discrete height are the same in each boundary subsection comprises the following steps:

for the nth hierarchy, when the polynomial order of the dynamic linear system optimization model is 3, the corresponding constraint conditions are:

wherein (h) _n1 、h _n2 ) Respectively the lower and upper boundary values of the nth hierarchical polynomial at the discrete height of the lower boundary subsection, (h) _nd 、h _nd-1 ) Upper and lower boundary values of discrete heights of the nth hierarchical polynomial upper boundary subsection, S _Pn ”'(h _n1 )、S _ρn ”'(h _n1 )、S _Tn ”'(h _n1 ) Nth layered plurality of atmospheric pressure, vapor density, and atmospheric temperature, respectivelyAt a discrete height h _n1 The third order derivative value at that boundary condition, the other variables in the boundary condition, are the third order derivative values at other discrete height values. Establishing a linear equation set of a multi-element variable by using constraint conditions and boundary conditions, and solving to obtain coefficients of each sub-segment linear polynomial; coefficients of all sub-segment linear polynomials constitute coefficients of the hierarchical polynomial;

s61, processing continuous altitude atmosphere parameter information in the satellite terahertz communication link range by using a terahertz dry air absorption coefficient calculation model to obtain a terahertz communication frequency band dry air absorption coefficient gamma with a set altitude _dry The method comprises the steps of carrying out a first treatment on the surface of the The terahertz dry air absorption coefficient calculation model has the expression:

s62, processing continuous altitude atmosphere parameter information in the range of the satellite terahertz communication link by using a terahertz communication frequency band water vapor absorption coefficient calculation model to obtain a terahertz communication frequency band water vapor absorption coefficient with a set altitudeThe terahertz communication frequency band water vapor absorption coefficient calculation model has the expression:

wherein ρ is the set height of the water vapor density;

and S63, summing the terahertz communication frequency band dry air absorption coefficient and the terahertz communication frequency band water vapor absorption coefficient to obtain an atmospheric attenuation coefficient gamma of the terahertz communication frequency band. The process can be expressed as

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.

Claims

1. The method for calculating the atmospheric transmission loss of the terahertz communication channel of the satellite is characterized by comprising the following steps of:

s7, processing the atmospheric attenuation coefficient of the terahertz communication frequency band and the transmission distance of the satellite communication link by using a terahertz channel atmospheric transmission loss calculation model to obtain the satellite terahertz communication channel atmospheric transmission loss value;

S26, deleting the height range information of the layered connection part of each type of atmosphere parameters from the initial layered height range information to obtain layered height range information of each type of atmosphere parameters;

s33, processing discrete height information of the layered junction by using the curvature dynamic optimal planning model to obtain an atmosphere parameter set of continuous height of the layered junction;

s43, processing discrete height information of the layering height range by using the dynamic linear system optimization model, and calculating to obtain an atmosphere parameter information set of continuous heights in the layering height range;

wherein ρ is the set height of the water vapor density;

s63, summing the terahertz communication frequency band dry air absorption coefficient and the terahertz communication frequency band water vapor absorption coefficient to obtain an atmospheric attenuation coefficient gamma of the terahertz communication frequency band;

2. The method for calculating the atmospheric transmission loss of the terahertz communication channel of claim 1, wherein the discrete data information at the layered junction is processed by using a curvature statistical analysis method and a first iterative solution model to construct a curvature dynamic optimal planning model, and the method comprises the following steps:

3. The method for calculating the atmospheric transmission loss of the terahertz communication channel of claim 1, wherein the discrete data information at the layered junction is processed by using a curvature statistical analysis method and a first iterative solution model to construct a curvature dynamic optimal planning model, and the method comprises the following steps:

s321, integrating the discrete height information of the layered connection part, and establishing a discrete height set of the layered connection part, wherein the height information set of the mth layered connection part is expressed as H _m ＝{h _m1 ,h _m2 ,...,h _ms (wherein s is each of the components)The number of discrete heights of the layers;

P _m ＝{p _m1 ,p _m2 ,...,p _ms }、ρ _m ＝{ρ _m1 ,ρ _m2 ,...,ρ _ms }、T _m ＝{t _m1 ,t _m2 ,...,t _ms }，

wherein p is _mi 、ρ _mi 、t _mi I-th data representing discrete sets of parameter information for atmospheric pressure, vapor density, atmospheric temperature, i=1, 2, …, s, respectively, at the mth layered junction;

wherein, the mth layered junction is for atmospheric pressure, water vapor density and largeThe order of the polynomials determined by the gas temperature are s1, s2 and s3 respectively, and the initial polynomials determined are G respectively _Pm0 (h _m )，G _ρm0 (h _m )，G _Tm0 (h _m )，h _m For the continuous height value of the mth layered junction, G _Pm0 (h _m )、G _ρm0 (h _m )、G _Tm0 (h _m ) An initial polynomial of atmospheric pressure, vapor density and atmospheric temperature, G, respectively, at the mth layered junction _Pm1 (h _m )、G _ρm1 (h _m )、G _Tm1 (h _m ) 1 st order polynomials of atmospheric pressure, water vapor density and atmospheric temperature at the mth layered junction, G _Pm(k+1) (h _m )、G _ρm(k+1) (h _m )、G _Tm(k+1) (h _m ) A k+1st order polynomial of the atmospheric pressure, the water vapor density and the atmospheric temperature, respectively, (u) _Pmk 、v _Pm(k-1) )、(u _ρmk 、v _ρm(k-1) )、(u _Tmk 、v _Tm(k-1) ) Internal coefficients of the k+1st order polynomial of the atmospheric pressure, vapor density and atmospheric temperature, u _Pm0 、u _ρm0 、u _Tm0 The internal coefficients of the 1 st order polynomials of atmospheric pressure, vapor density and atmospheric temperature, respectively;

4. The method for calculating the atmospheric transmission loss of the terahertz communication channel of claim 1, wherein the discrete data information in the layered height range is processed by using a slope statistical analysis method and a second iterative solution model, and the method for constructing and obtaining a dynamic linear system optimization model comprises the following steps:

5. The method for calculating the atmospheric transmission loss of the terahertz communication channel of claim 4, wherein the discrete data information in the layered height range is processed by using a slope statistical analysis method and a second iterative solution model to construct a dynamic linear system optimization model, and the method comprises the following steps:

s422, for the discrete atmosphere parameters of the layering height rangeIntegrating information, and respectively establishing corresponding discrete atmosphere parameter information sets in a layered height range for each type of atmosphere parameters; the information sets of three atmosphere parameters including discrete atmospheric pressure, water vapor density and atmospheric temperature in the nth layering height range are respectively defined as P _n 、ρ _n 、T _n The expression is:

S427, for each type of atmosphere parameterNumber, set a second range threshold a ₃ Difference of the other delta ₂ And mean mu ₂ Ratio k of (2) ₂ To make a discrimination when k ₂ ≤a ₃ Determining the polynomial order of the dynamic linear system optimization model to be 3; when a is ₃ ＜k ₂ Determining the polynomial order of the dynamic linear system optimization model to be 5;