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Mesosphere-Stratosphere-Troposphere(MST) Radar Data Analysis

In this example we  analyse data  from MST (mesosphere-stratosphere-troposphere) radar observations. MST radars used to do observation of the dynamics of the lower and middle atmosphere to study winds, waves, turbulence and instabilities generate irregularities in the atmosphere. The reflected radar signals from the random irregularities are collected by the receiver antenna. The return signal strength is highly depending on the refractive index which is a function of atmospheric parameters such as humidity, temperature, and pressure and electron density. Hence those parameters will highly affect the signal to noise ratio (SNR). Input data file structure {UT, Altitude,Signal amplitude (linear),Signal-to-noise ratio (SNR), dB, Zonal wind, m/s, Meridional wind, m/s}

clear all; clc; close all;
% Data files name ID
fl = {'150 m height resolutions';
      '1200 m height resolutions';
      'Barker Coding';
      'Complementary Coding';
      'Uncoded Data';
    };
% Files for three different observation days
fid  = { 'TXT_20061211_test1.fca','TXT_20071010_test1.fca',...
                    'TXT_20081014_test1.fca';   % 150 m height resolutions
        'TXT_20061211_test2.fca','TXT_20071010_test2.fca',...
                    'TXT_20081014_test2.fca';   % 1200 m height resolutions
       'TXT_20061211_test3.fca','TXT_20071010_test3.fca',...
                    'TXT_20081014_test3.fca';   % Barker coding
        'TXT_20061211_test4.fca','TXT_20071010_test4.fca',...
                    'TXT_20081014_test4.fca';   % Complementary coding
        'TXT_20061211_test5.fca','TXT_20071010_test5.fca',...
                    'TXT_20081014_test5.fca';   % Uncoded data
};
dates = ['2006 Dec 11'; '2007 Oct 10'; '2008 Dec 14' ];

SNR value as a function of universal time and altitude for three different observation days

fs = size(fid,1);
fd = size(fid,2);
for jd = 1:fd
    for id = 1:fs
        clear SNR;
        fname = fid{id,jd};
        data    = load(fname);
        time    = unique(data(:,1));
        alt     = unique(data(:,2));
        size_t  = size(time,1);
        size_a  = size(alt,1);
        % Signal-to-noise ratio (SNR), dB
        for i = 1:size_a
            for j = 1:size_t
                    SNR(i,j)= data(i+((j-1)*size_a),4);
            end
        end
        % Plot
        if(id > 2 )
            FigHandle = figure(2);
            hold on;
            ii = id - 2;
            subplot(3,3,ii+(jd-1)*3);
            colormap(jet);
            pcolor(time,alt,SNR);
            shading flat;
            caxis([-25,30]);
            if(id == 5)
             colorbar;
            end
            if(id == 3)
                ylabel('Altitude [km]');
            end
            if((id == 4))
                xlabel(['UT/Date: ',dates(jd,:) ]);
            end
                if((jd == 1)&&(id == 4) )
                    title(['Signal-to-Noise Ratio(SNR) [dB]', fl(id)]);
                else
                       if(jd == 1)
                           title(fl(id));
                       end
                end
            set(FigHandle, 'Position', [100, 0, 800, 800]);
           else
                FigHandle = figure(1);
                hold on;
                subplot(3,2,id+(jd-1)*2);
                colormap(jet);
                pcolor(time,alt,SNR);
                shading flat;
                caxis([-25,30]);
                if(id == 2)
                    colorbar;
                end
                if(id == 1)
                    ylabel('Altitude [km]');
                end
                xlabel(['UT/Date: ',dates(jd,:) ]);
                if((jd == 1))
                    title(['Signal-to-Noise Ratio(SNR) [dB]', fl(id)]);
                end
            set(FigHandle, 'Position', [100, 0, 600, 800]);
        end
    end
endSR_mod2_01 SR_mod2_02

Magnitude and direction of horizontal winds

The figures below provide the horizontal wind variation over the altitude and universal time for three different days. The direction of the wind is mostly from west to east. The results are expected as the wind in this layer of atmosphere moves west to east because of the Coriolis acceleration due to force caused by the rotation of the earth.

clear all; clc; close all;
dates = ['2006 Dec 11'; '2007 Oct 10'; '2008 Dec 14' ];
% Data files name ID
fid = {'TXT_20061211_test4.fca','TXT_20071010_test4.fca',...
                    'TXT_20081014_test4.fca' };     % Complementary coding
% Radar with Complementary coding
for id = 1:3
data   = load(fid{id});
time   = unique(data(:,1));
alt    = unique(data(:,2));
size_t = size(time,1);
size_a = size(alt,1);
% Signal-to-noise ratio (SNR), dB
for i = 1:size_a
    for j = 1:size_t
            zWind(i,j)= data(i+((j-1)*size_a),5); % Zonal Wind, East
            mWind(i,j)= data(i+((j-1)*size_a),6); % Meridional Wind, North
    end
end
hWind = sqrt(zWind.^2 + zWind.^2);       % Horizontal Wind [m/s]
%dWind=  atan2(zWind,mWind)*180/pi + 180; % Wind Direction angle, Zero in North direction
a_max = 60;
a_min = 30;
FigHandle = figure(2 + id);
set(FigHandle, 'Position', [100, 0, 800, 400]);
subplot(2,2,[1 3]);
colormap(cool);
pcolor(time,alt(1:a_max),hWind(1:a_max,:));
shading flat;
colorbar;
xlabel(['UT/Date:',dates(id,:)]);
ylabel('Altitude [km]');
title('Horizontal wind speed [m/s], Complementary coded signal');
subplot(2,2,[2 4]);
hold on;
whitebg([0.0 .0 .2]);
quiver(time,alt(1:a_max),zWind(1:a_max,:),mWind(1:a_max,:),'r');
%contour(time,alt(1:a_max),hWind(1:a_max,:));
xlabel(['UT/Date:',dates(id,:)]);
title('Horizontal wind direction(top - North,right - East)');
axis([min(time),max(time),min(alt(1:a_max)),max(alt(1:a_max))]);
hold off;
end

 

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AGI STK 10 MATLAB INTERFACE: Satellite Ground Track

close all; clear all; clc

 AGI STK, is  as a software package from Analytical Graphics, Inc.(AGI) that
 allows to perform complex analyses of ground, sea, air, and space
 missions. More information can be found in AGI website.  https://www.agi.com
 To create this code we used educational code samples from % https://www.agi.com/resources/

% Establish the connection AGI STK 10
try
    % Grab an existing instance of STK 10
    uiapp = actxGetRunningServer('STK10.application');
catch
    % STK is not running, launch new instance
    % Launch a new instance of STK10 and grab it
    uiapp = actxserver('STK10.application');
end
%get the root from the personality
%it has two... get the second, its the newer STK Object Model Interface as
%documented in the STK Help
root = uiapp.Personality2;

% set visible to true (show STK GUI)
uiapp.visible = 1;
% From the STK Object Root you can command every aspect of the STK GUI
% close current scenario or open new one
try
    root.CloseScenario();
    root.NewScenario('SatelliteGroundTrack');
catch
    root.NewScenario('SatelliteGroundTrack');
end
% Set units to utcg before setting scenario time period and animation period
root.UnitPreferences.Item('DateFormat').SetCurrentUnit('UTCG');
% %set units to epoch seconds because this works the easiest in matlab
% root.UnitPreferences.Item('DateFormat').SetCurrentUnit('EPSEC');

% Set scenario time period and animation period
root.CurrentScenario.SetTimePeriod('25 May 2013 12:00:00.000', '26 May 2013 12:00:00.000');
root.CurrentScenario.Epoch = '25 May 2013 12:00:00.000';

% Create satellite
satObj = root.CurrentScenario.Children.New('eSatellite', 'SmallSats1');

% Propagate satellite
satObj.Propagator.InitialState.Representation.AssignClassical(...
    'eCoordinateSystemJ2000', 6750, 0.1, 53.4, 0, 0, 0);
% CoordinateSystem, Semimajor Axis, Eccentricity, Inclination,
% Arg. of Perigee, RAAN, Mean Anomaly

satObj.Propagator.StartTime = '25 May 2013 12:00:00.000';
satObj.Propagator.StopTime  = '25 May 2013 15:00:00.000';
satObj.Propagator.Propagate;

% Get Latitude, Longitude  for the satellite over the course of the mission.
LLAState = satObj.DataProviders.Item('LLA State').Group.Item('Fixed');
Elems = {'Time';'Lat';'Lon'};
satStartTime = satObj.Propagator.EphemerisInterval.FindStartTime;
satStopTime = satObj.Propagator.EphemerisInterval.FindStopTime;
Results = LLAState.ExecElements(satStartTime, satStopTime, 10, Elems);
time = cell2mat(Results.DataSets.GetDataSetByName('Time').GetValues);
Lat  = cell2mat(Results.DataSets.GetDataSetByName('Lat').GetValues);
Long = cell2mat(Results.DataSets.GetDataSetByName('Lon').GetValues);

Plot

figure(1);
hold on;
axis([0 360 -90 90]);
load('topo.mat','topo','topomap1');
contour(0:359,-89:90,topo,[0 0],'b')
axis equal
box on
set(gca,'XLim',[-180 180],'YLim',[-90 90], ...
    'XTick',[-180 -120 -60 0 60 120 180], ...
    'Ytick',[-90 -60 -30 0 30 60 90]);
image([-180 180],[-90 90],topo,'CDataMapping', 'scaled');
colormap(topomap1);
ylabel('Latitude [deg]');
xlabel('Longitude [deg]');
title('Satellite Ground Track');
scatter(Long,Lat,'.r');

Simplesat_01Ground track generated by AGI STK10  

Relative Motion of Satellites, Numerical Simulation

This example shows how to use the state vectors of spacecraft A and B to find the position, velocity and acceleration of B relative to A in the LVLH frame attached to A. We numerically solve fundamental equation of relative two-body motion to obtain the trajectory of spacecraft B relative to spacecraft A and the distance between two satellites. For more details about the theory and algorithm look at Chapter 7 of H. D. Curtis, Orbital Mechanics for Engineering Students,Second Edition, Elsevier 2010

clc;
clear all;
% Initial State vectors of Satellite A and B
RA0 = [-266.77,  3865.8, 5426.2];     % [km]
VA0 = [-6.4836, -3.6198, 2.4156];     % [km/s]
RB0 = [-5890.7, -2979.8, 1792.2];     % [km]
VB0 = [0.93583, -5.2403, -5.5009];    % [km/s]
mu  = 398600;            % Earth’s gravitational parameter [km^3/s^2]
t   = 0;                 % initial time
dt  = 15;                % Simulation time step [s]
dT  = 4*24*3600;         % Simulation interval  [s]
% Using fourth-order Runge–Kutta method to solve fundamental equation
% of relative two-body motion
F_r = @(R) -mu/(norm(R)^3)*R;
VA = VA0; RA  = RA0;
VB = VB0; RB  = RB0;
ind = 1;
while (t <= dT)
    % Relative position
    hA = cross(RA, VA);     % Angular momentum of A
    % Unit vectors i, j,k of the co-moving frame
    i = RA/norm(RA);  k = hA/norm(hA); j = cross(k,i);
    % Transformation matrix Qxx:
    QXx   = [i; j; k];
    Om    = hA/norm(RA)^2;
    Om_dt = -2*VA*RA'/norm(RA)^2.*Om;
    % Accelerations of A and B,inertial frame
    aA = -mu*RA/norm(RA)^3;
    aB = -mu*RB/norm(RB)^3;
    % Relative position,inertial frame
    Rr = RB - RA;
    % Relative position,LVLH frame attached to A
    R_BA(ind,:) = QXx*Rr';

    % A Satellite
    k_1 = dt*F_r(RA);  k_2 = dt*F_r(RA+0.5*k_1);
    k_3 = dt*F_r(RA+0.5*k_2);  k_4 = dt*F_r(RA+k_3);
    VA  = VA + (1/6)*(k_1+2*k_2+2*k_3+k_4);
    RA  = RA + VA*dt;
    % B Satellite
    k_1 = dt*F_r(RB);  k_2 = dt*F_r(RB+0.5*k_1);
    k_3 = dt*F_r(RB+0.5*k_2);     k_4 = dt*F_r(RB+k_3);
    VB = VB + (1/6)*(k_1+2*k_2+2*k_3+k_4);
    RB  = RB + VB*dt;

    R_A(ind,:)  = RA;
    R_B(ind,:)  = RB;
    time(ind)   = t;
    t   = t+dt;
    ind = ind+1;
end
r_BA = (R_BA(:,1).^2+R_BA(:,2).^2+R_BA(:,3).^2).^0.5;
close all;
figure(1);
hold on;
plot3(R_A(:,1),R_A(:,2),R_A(:,3),'r');
plot3(R_B(:,1),R_B(:,2),R_B(:,3),'y');
title('Satellites orbits around earth');
legend('Satellite A','Satellites B');
xlabel('km');ylabel('km');
% Plotting Earth
load('topo.mat','topo','topomap1');
colormap(topomap1);
% Create the surface.
radius_earth=6378;
[x,y,z] = sphere(50);
x =radius_earth*x;
y =radius_earth*y;
z =radius_earth*z;
props.AmbientStrength = 0.1;
props.DiffuseStrength = 1;
props.SpecularColorReflectance = .5;
props.SpecularExponent = 20;
props.SpecularStrength = 1;
props.FaceColor= 'texture';
props.EdgeColor = 'none';
props.FaceLighting = 'phong';
props.Cdata = topo;
surface(x,y,z,props);
hold off;

figure(2);
plot3(R_BA(:,1),R_BA(:,2),R_BA(:,3),'k');
title('The trajectory of spacecraft B relative to spacecraft A');
xlabel('km');ylabel('km');zlabel('km');

figure(3);
plot(time/3600,r_BA);
title('Distance between two satellites');
xlabel('hour');ylabel('km')

min_r = min(r_BA);
max_r = max(r_BA);
fprintf('Max distance between two satellites %6.4f km \n',max_r);
fprintf('Min distance between two satellites %6.4f km \n',min_r);
Max distance between two satellites 13850.3054 km 
Min distance between two satellites 262.0271 km

Relative_motion_sim_01 Relative_motion_sim_02 Relative_motion_sim_03

Relative Motion of Satellites

clc; clear all; close all;

% This example shows how to use the state vectors of spacecraft A and B
% to find the position, velocity and acceleration of B relative
% to A in the LVLH frame attached to A. For more details about the theory and
% algorithm look at Chapter 7 of  H. D. Curtis, Orbital Mechanics for
% Engineering Students,Second Edition, Elsevier 2010

Input

State vectors of Satellite A and B

RA = [-266.77,  3865.8, 5426.2];     % [km]
VA = [-6.4836, -3.6198, 2.4156];     % [km/s]
RB = [-5890.7, -2979.8, 1792.2];     % [km]
VB = [0.93583, -5.2403, -5.5009];    % [km/s]

% Earth gravitational parameter
mu = 398600;                        % [km^3/s^2]

Algorithm

hA = cross(RA, VA);                 % Angular momentum of A
% Unit vectors i, j,k of the co-moving frame
i = RA/norm(RA);  k = hA/norm(hA); j = cross(k,i);

% Transformation matrix Qxx:
QXx   = [i; j; k];
Om    = hA/norm(RA)^2;
Om_dt = -2*VA*RA'/norm(RA)^2.*Om;

% Accelerations of A and B,inertial frame
aA = -mu*RA/norm(RA)^3;
aB = -mu*RB/norm(RB)^3;

% Relative position,inertial frame
Rr = RB - RA;
% Relative position,LVLH frame attached to A
R_BA = QXx*Rr';

% Relative velocity,inertial frame
Vr = VB - VA - cross(Om,Rr);
% Relative velocity,LVLH frame attached to A
V_BA = QXx*Vr';

% Relative acceleration, inertial frame
ar = aB - aA - cross(Om_dt,Rr) - cross(Om,cross(Om,Rr))- 2*cross(Om,Vr);
% Relative acceleration,LVLH frame attached to A
a_BA = QXx*ar';

fprintf('Position of B relative to A in LVLH frame attached to A \n');
fprintf('R_BA = [%4.2f %4.2f %4.2f] km \n\n', R_BA);

fprintf('Velocity of B relative to A in LVLH frame attached to A \n');
fprintf('V_BA = [%6.4f %6.4f %6.4f] km/s \n\n', V_BA);

fprintf('Acceleration of B relative to A in LVLH frame attached to A \n');
fprintf('a_BA = [%8.8f %8.8f %8.8f] km/s^2 \n', a_BA);
Position of B relative to A in LVLH frame attached to A 
R_BA = [-6701.22 6828.28 -406.24] km 

Velocity of B relative to A in LVLH frame attached to A 
V_BA = [0.3168 0.1120 1.2470] km/s 

Acceleration of B relative to A in LVLH frame attached to A 
a_BA = [-0.00022213 -0.00018083 0.00050590] km/s^2

 

Orbital Inclination Change

Transfer from a LEO 350 km circular orbit with 53.4 deg inclination to a Geostationary Equatorial Orbit(GEO)

clear all; clc;
close all;

Input

R_LEO = 6378 + 350;      % km
R_GEO = 42164;           % km
mu    = 398600;          % km^3/s^2
incl  = 53.4;            % deg

Method 1: Hohman transfer from LEO to GEO and after inclination change

Rp  = R_LEO;
Ra  = R_GEO;
e   = (Ra - Rp)/(Ra + Rp);  % transfer orbit eccentricity
a   = (Ra + Rp)/2;          % transfer orbit semimajor axis
V_LEO   = (mu/R_LEO)^0.5;
Vp      = (2*mu/R_LEO - mu/a)^0.5;
Va      = (2*mu/R_GEO - mu/a)^0.5;
V_GEO   = (mu/R_GEO)^0.5;
dV_LEO  = abs(Vp - V_LEO);
dV_GEO  = abs(V_GEO - Va);
dV_Hoff = dV_GEO + dV_LEO;

% Inclination change at GEO
dV_incl = 2*V_GEO*sind(incl/2);
dV_total1 = dV_incl + dV_Hoff;

fprintf('Method 1: Hohman transfer from LEO to GEO and after inclination change\n');
fprintf('dV_Hoff  = %6.2f [km/s] \n',dV_Hoff);
fprintf('dV_incl  = %6.2f [km/s] \n',dV_incl);
fprintf('dV_total = %6.2f [km/s] \n',dV_total1);
Method 1: Hohman transfer from LEO to GEO and after inclination change
dV_Hoff  =   3.87 [km/s] 
dV_incl  =   2.76 [km/s] 
dV_total =   6.64 [km/s]

Method 2: Inclination change in LEO and after Hohman transfer to GEO

dV_incl   = 2*V_LEO*sind(incl/2);
dV_total2 = dV_incl + dV_Hoff;

fprintf('\nMethod 2: Inclination change in LEO and after Hohman transfer to GEO\n');
fprintf('dV_incl  = %6.2f [km/s] \n',dV_incl);
fprintf('dV_Hoff  = %6.2f [km/s] \n',dV_Hoff);
fprintf('dV_total = %6.2f [km/s] \n\n',dV_total2);
fprintf('Method 2/Method 1 = %6.2f \n',dV_total2/dV_total1);
Method 2: Inclination change in LEO and after Hohman transfer to GEO
dV_incl  =   6.92 [km/s] 
dV_Hoff  =   3.87 [km/s] 
dV_total =  10.79 [km/s] 

Method 2/Method 1 =   1.63

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