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ObjectTracker.m
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ObjectTracker.m
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clear; clc; close all;
[dev_mode, filter_type, traj, run_mode, sonar, forced] = TrackerUI();
% cd C:\Users\Andrei\Desktop\Licenta+Lucru\Personale\KalmanFilter\MatlabTracker\ObjectTracking\
% d = System.IO.File.GetCreationTime(cd);
% file = dir('ObjectTracker.m');
% creationDateTime = datetime(d.Year, d.Month, d.Day, d.Hour, d.Minute, d.Second);
% fprintf('Welcome to the 2D Shark Chase Simulator \n \n');
% fprintf('Author: Moraru Andrei \n');
% fprintf('Created on: %s \n', datestr(creationDateTime));
% fprintf('Last modified: %s \n\n', file.date);
if strcmpi(run_mode,'cmdrive') == 1
try
port = serialportlist;
ard = arduino(port,'Nano3');
fs = 200;
mpu = mpu6050(ard,'SampleRate',fs);
ard_kalman_filter = QuaternionKalmanFilterModel();
phi = 0;
theta = 0;
psi = 0;
ard_Q = 9 * eye(4);
ard_R = 0.09 * eye(4);
ard_P = 9 * eye(4);
ard_q1Std = ard_Q(1,1); ard_q2Std = ard_Q(2,2); ard_q3Std = ard_Q(3,3); ard_q4Std = ard_Q(4,4);
ard_a1Std = ard_R(1,1); ard_a2Std = ard_R(2,2); ard_a3Std = ard_R(3,3); ard_a4Std = ard_R(4,4);
ard_init_q1_std = ard_P(1,1); ard_init_q2_std = ard_P(2,2); ard_init_q3_std = ard_P(3,3); ard_init_q4_std = ard_P(4,4);
ard_init_on_measurement = false;
ard_dt = 1/fs;
traj = 'random';
catch ConnectionError
run_mode ='simu';
end
end
if dev_mode == 1
while 1
prompt = 'Pick your difficulty level: [L/E/U] ';
filter_type = input(prompt,'s');
if (filter_type == 'E' || filter_type == 'e')
break;
end
if (filter_type == 'L' || filter_type == 'l')
break;
end
if (filter_type == 'U' || filter_type == 'u')
break;
end
if (filter_type ~= 'L' && filter_type ~= 'l' && filter_type ~= 'E' && filter_type ~= 'e' && filter_type ~= 'U' && filter_type ~= 'u')
disp('Not a viable choice')
end
end
fprintf('\n')
while 1
prompt = 'Would you like to make changes to the model? [Y/N] ';
str = input(prompt,'s');
if (str == 'Y' || str == 'y')
break;
end
if (str == 'N' || str == 'n')
initial_x_position = 0;
initial_y_position = initial_x_position;
initial_heading = 45;
initial_speed = 4000;
accel_std = 20;
meas_std = 10;
init_pos_std = 5;
init_vel_std = 0.5;
init_yaw_std = 0.005;
yaw_std = 0.0095;
init_on_measurement = false;
max_blood_units = 100;
motion_type = 'linear';
break;
end
if (str ~= 'N' && str ~= 'Y' && str ~= 'n' && str ~= 'y')
disp('Not a viable choice')
end
end
fprintf('\n')
end
if (filter_type == 'L' || filter_type == 'l')
vehicle_model = VehicleModel2D();
chaser_model = VehicleModel2D();
kalman_filter = KalmanFilterModel();
seagull = 0;
end
if (filter_type == 'E' || filter_type == 'e')
vehicle_model = ExtendedVehicleModel2D();
chaser_model = VehicleModel2D();
kalman_filter = ExtendedKalmanFilterModel();
end
if (filter_type == 'U' || filter_type == 'u')
vehicle_model = UnscentedVehicleModel2D();
chaser_model = VehicleModel2D();
kalman_filter = UnscentedKalmanFilterModel();
end
if dev_mode == 1
if (str == 'Y' || str == 'y')
while 1
prompt = 'Set the initial position of the seal on X axis: ';
initial_x_position = input(prompt);
if (~isnumeric(initial_x_position))
disp("That's not a good idea")
else
break;
end
end
while 1
prompt = 'Set the initial position of the seal on Y axis: ';
initial_y_position = input(prompt);
if (~isnumeric(initial_y_position))
disp("That's not a good idea")
else
break;
end
end
while 1
prompt = 'Set the initial speed of the seal [m/s]: ';
initial_speed = input(prompt);
if (initial_speed < 1000)
initial_speed = initial_speed * 100;
end
if (~isnumeric(initial_speed))
disp("That's not a good idea")
else
break;
end
end
while 1
prompt = 'Set the initial heading of the seal [degrees]: ';
initial_heading = input(prompt);
if (~isnumeric(initial_heading))
disp("That's not a good idea")
else
break;
end
end
while 1
prompt = 'Set maximum blood loss units available for the seal: ';
max_blood_units = input(prompt);
if (~isnumeric(initial_x_position))
disp("That's not a good idea")
else
break;
end
end
while 1
prompt = 'Set the motion type: [linear / circle / random] ';
motion_type = input(prompt);
fprintf('\n')
if (~isstring(motion_type) && ~ischar(motion_type))
disp("That's not a good idea")
else
break;
end
end
end
while 1
prompt = 'Proceed to tune the filter? [Y/N] ';
str = input(prompt,'s');
if (str == 'Y' || str == 'y')
break;
end
if (str == 'N' || str == 'n')
accel_std = 5;
meas_std = 10;
init_pos_std = 5;
init_vel_std = 0.5;
init_yaw_std = 0.005;
yaw_std = 0.005;
init_on_measurement = false;
break;
end
if (str ~= 'N' && str ~= 'Y' && str ~= 'n' && str ~= 'y')
disp('Not a viable choice')
end
end
if (str == 'Y' || str == 'y')
while 1
prompt = 'Set the acceleration standard deviation [m/s^2]: ';
accel_std = input(prompt);
if (~isnumeric(accel_std))
disp("That's not a good idea")
else
break;
end
end
if (filter_type == 'E' || filter_type == 'e') || (filter_type == 'U' || filter_type == 'u')
while 1
prompt = 'Set the yaw standard deviation [degrees]: ';
yaw_std = deg2rad(input(prompt));
if (~isnumeric(yaw_std))
disp("That's not a good idea")
else
break;
end
end
end
while 1
prompt = 'Set the measurement standard deviation [m]: ';
meas_std = input(prompt);
if (~isnumeric(meas_std))
disp("That's not a good idea")
else
break;
end
end
while 1
prompt = 'Set the initial position standard deviation [m]: ';
init_pos_std = input(prompt);
if (~isnumeric(init_pos_std))
disp("That's not a good idea")
else
break;
end
end
while 1
prompt = 'Set the initial velocity standard deviation [m/s]: ';
init_vel_std = input(prompt);
if (~isnumeric(init_vel_std))
disp("That's not a good idea")
else
break;
end
end
if (filter_type == 'E' || filter_type == 'e') || (filter_type == 'U' || filter_type == 'u')
while 1
prompt = 'Set the initial yaw standard deviation [degrees]: ';
init_yaw_std = deg2rad(input(prompt));
if (~isnumeric(init_yaw_std))
disp("That's not a good idea")
else
break;
end
end
end
if (filter_type == 'L' || filter_type == 'l')
while 1
prompt = 'Start the shark chase on the first measurement? [True/False]: ';
init_on_measurement = input(prompt);
if (~islogical(init_on_measurement))
disp("That's not a good idea")
else
break;
end
end
end
end
fprintf('\n')
if (filter_type == 'E' || filter_type == 'e') || (filter_type == 'U' || filter_type == 'u')
init_on_measurement = true;
while 1
prompt = 'Activate seagull? [True/False]: ';
seagull = input(prompt);
if (~islogical(seagull))
disp("That's not a good idea")
else
break;
end
end
end
if (seagull == 1)
while 1
prompt = 'Set the range standard deviation [m]: ';
sgl_range_std = input(prompt);
if (~isnumeric(sgl_range_std))
disp("That's not a good idea")
else
break;
end
end
while 1
prompt = 'Set the heading standard deviation [degrees]: ';
sgl_heading_std = deg2rad(input(prompt));
if (~isnumeric(sgl_heading_std))
disp("That's not a good idea")
else
break;
end
end
end
end
if dev_mode == 0
initial_x_position = 50;
initial_y_position = initial_x_position;
initial_heading = 45;
initial_speed = 4500;
accel_std = 20;
meas_std = 10;
init_pos_std = 5;
init_vel_std = 0.5;
init_yaw_std = 0.0095;
yaw_std = 0.005;
init_on_measurement = false;
max_blood_units = 120;
seagull = sonar;
if seagull
sgl_range_std = 10;
sgl_heading_std = deg2rad(0.005);
end
motion_type = traj;
end
fprintf('\n')
measurement_noise_std = 300;
heading_noise = randn() * deg2rad(0.05);
acc_noise = randn() * 20;
vehicle_params = struct('initial_x_position',initial_x_position,'initial_y_position',initial_y_position,...
'initial_heading', deg2rad(initial_heading),'initial_speed',initial_speed, 'heading_noise', heading_noise, 'acc_noise',acc_noise);
vehicle_model.initialise(vehicle_params);
initial_measurement = [vehicle_model.x_pos + randn() * measurement_noise_std;...
vehicle_model.x_pos + randn() * measurement_noise_std];
if init_on_measurement
chaser_params = struct('initial_x_position',initial_measurement(1),'initial_y_position',initial_measurement(2),...
'initial_heading', 0,'initial_speed',0, 'heading_noise', heading_noise, 'acc_noise',acc_noise);
else
chaser_params = struct('initial_x_position',0,'initial_y_position',0,...
'initial_heading', 0,'initial_speed',0, 'heading_noise', heading_noise, 'acc_noise',acc_noise);
end
chaser_model.initialise(chaser_params);
if (filter_type == 'L' || filter_type == 'l')
kf_options = struct('accel_std',accel_std,'meas_std',meas_std,'init_on_measurement',init_on_measurement,...
'init_pos_std',init_pos_std,'init_vel_std',init_vel_std,'initial_measurement',initial_measurement);
end
if (filter_type == 'E' || filter_type == 'e') || (filter_type == 'U' || filter_type == 'u')
ekf_options = struct('accel_std',accel_std,'yaw_std',yaw_std,'meas_std',meas_std,...
'init_on_measurement',init_on_measurement, 'init_pos_std',init_pos_std,...
'init_vel_std',init_vel_std,'init_yaw_std',init_yaw_std,'initial_measurement',initial_measurement);
end
f = figure;
set(gcf, 'Position', get(0, 'Screensize'));
set(gca,'color',[0 0.4470 0.7410]);
set(gcf,'color',[0 0.4470 0.7410]);
[Seal,X,Y] = SealDrawer();
[Shark,Xs,Ys] = SharkDrawer();
if seagull
[Seagull,Xl,Yl] = SeagullDrawer();
for index = 1:length(Seagull)
Xl{index} = 300 * Xl{index};
Yl{index} = 300 * Yl{index};
end
end
for index = 1:length(Seal)
X{index} = 500 * X{index};
Y{index} = 500 * Y{index};
end
for index = 1:length(Shark)
Xs{index} = 400 * Xs{index};
Ys{index} = 400 * Ys{index};
end
dt = 0.15;
i = 1;
axis off ;
blood = 0;
counter_seconds = 0;
et = num2str(counter_seconds);
elapsed_time = annotation('textbox', [0, 0.5, 0, 0], 'string', et, 'FontSize', 20, 'Color', 'w');
innovation_history_x = [];
innovation_history_y = [];
chaser_error_history = [];
estimation_error_history = [];
estimated_covariance_history_x_pos = [];
estimated_covariance_history_y_pos = [];
estimated_covariance_history_x_vel = [];
estimated_covariance_history_y_vel = [];
estimated_covariance_history_vel = [];
estimated_covariance_history_yaw = [];
vehicle_position_history_x = [];
vehicle_position_history_y = [];
vehicle_velocity_history_x = [];
vehicle_velocity_history_y = [];
vehicle_heading_history = [];
vehicle_velocity_history = [];
chaser_position_history_x = [];
chaser_position_history_y = [];
chaser_velocity_history_x = [];
chaser_velocity_history_y = [];
chaser_acceleration_history_x = [];
chaser_acceleration_history_y = [];
vehicle_acceleration_history_x = [];
vehicle_acceleration_history_y = [];
measurement_history_x = [];
measurement_history_y = [];
estimated_state_history_x_pos = [];
estimated_state_history_y_pos = [];
estimated_state_history_x_vel = [];
estimated_state_history_y_vel = [];
estimated_state_history_vel = [];
estimated_state_history_yaw = [];
MP=get(0,'MonitorPositions');
if size(MP,1)>1
pos=get(f,'Position');
set(f,'Position',[pos(1,2)+MP(2,1:2) pos(3:4)]);
end
N = size(MP,1);
newPosition = MP(1,:);
if size(MP,1) == 1
else
newPosition(1) = newPosition(1) + MP(N,1);
end
f.set('Position', newPosition, 'units', 'normalized');
f.WindowState = 'maximized';
sgl = 0;
draw_sgl = 1;
kappa = 0;
axis tight
while ishandle(f)
if strcmpi(run_mode,'cmdrive') == 1
matr = table2array((read(mpu)));
accel = matr(1:end,1:3);
gyro = matr(1:end,4:6);
acc_x = accel(end,1);
acc_y = accel(end,2);
acc_z = accel(end,3);
gx = rad2deg(gyro(end,2));
gy = rad2deg(gyro(end,1));
gz = rad2deg(gyro(end,3));
gyro_x = gx + gy * tand(theta) * sind(phi) + gz * tand(theta) * cosd(phi);
gyro_y = gy * cosd(phi) - gz * sind(phi);
gyro_z = gz;
omega_body = ([gyro_x; gyro_y; gyro_z]);
acc_total_vector = sqrt((acc_x*acc_x)+(acc_y*acc_y)+(acc_z*acc_z));
angle_Roll_acc = (asind(double(acc_x)/acc_total_vector));
angle_Pitch_acc = (asind(double(acc_y)/acc_total_vector));
accel_angle = [angle_Roll_acc; angle_Pitch_acc];
yaw_rate = deg2rad(gx*0.05 - 20);
end
if strcmpi(run_mode,'simu') == 1
yaw_rate = deg2rad(randn()*0.05 - 20);
acc_x = randn() * 20;
acc_y = randn() * 1.5 * 10;
end
if (filter_type == 'L' || filter_type == 'l')
vehicle_acceleration_history_x = [vehicle_acceleration_history_x, acc_x];
vehicle_acceleration_history_y = [vehicle_acceleration_history_x, acc_y];
else
vehicle_acceleration_history_x = [vehicle_acceleration_history_x, acc_x * cos(vehicle_model.yaw)];
vehicle_acceleration_history_y = [vehicle_acceleration_history_x, acc_x * sin(vehicle_model.yaw)];
end
if i == 1
if strcmpi(run_mode,'cmdrive') == 1
ard_kalman_filter.initialise(ard_q1Std, ard_q2Std, ard_q3Std, ard_q4Std, ard_a1Std, ard_a2Std, ard_a3Std, ard_a4Std,...
ard_init_on_measurement, ard_init_q1_std, ard_init_q2_std, ard_init_q3_std, ard_init_q4_std);
Roll = 0;
Pitch = 0;
end
if (filter_type == 'L' || filter_type == 'l')
kalman_filter.initialise(dt,kf_options.accel_std,kf_options.meas_std,kf_options.init_on_measurement,...
kf_options.init_pos_std,kf_options.init_vel_std,kf_options.initial_measurement);
end
if (filter_type == 'E' || filter_type == 'e')
kalman_filter.initialise(dt,ekf_options.accel_std,ekf_options.yaw_std,ekf_options.meas_std,...
ekf_options.init_on_measurement,ekf_options.init_pos_std,ekf_options.init_vel_std,...
ekf_options.init_yaw_std,ekf_options.initial_measurement);
end
if (filter_type == 'U' || filter_type == 'u')
kalman_filter.initialise(ekf_options.accel_std,ekf_options.yaw_std,ekf_options.meas_std,...
ekf_options.init_on_measurement,ekf_options.init_pos_std,ekf_options.init_vel_std,...
ekf_options.init_yaw_std,ekf_options.initial_measurement);
end
else
if strcmpi(run_mode,'cmdrive') == 1
ard_kalman_filter.prediction(omega_body,ard_dt);
ard_kalman_filter.update(accel_angle);
real_angle = ard_kalman_filter.attitude_state;
phi = (real_angle(1));
theta = (real_angle(2));
psi = (real_angle(3));
Roll = -phi;
Pitch = -theta;
if i == 2
bias_Roll = Roll;
bias_Pitch = Pitch;
end
end
if strcmpi(motion_type,'linear') == 1
yaw_rate = 0;
if (filter_type == 'L' || filter_type == 'l')
vehicle_model.update_vehicle(dt,abs(acc_x),abs(acc_y),yaw_rate);
else
vehicle_model.update_vehicle(dt,abs(acc_x),yaw_rate)
end
else
if (filter_type ~= 'L' && filter_type ~= 'l')
vehicle_model.update_vehicle(dt,abs(acc_x),yaw_rate);
end
end
for j = 1:length(Seal)
Seal(j).YData = Y{j} + vehicle_model.y_pos;
Seal(j).XData = X{j} + vehicle_model.x_pos;
end
mx = vehicle_model.x_pos + randn() * measurement_noise_std;
my = vehicle_model.y_pos + randn() * measurement_noise_std;
if (filter_type == 'L' || filter_type == 'l')
if forced == 0
kalman_filter.prediction_step();
else
kalman_filter.forced_prediction_step([acc_x;acc_x]);
end
end
if (filter_type == 'E' || filter_type == 'e') || (filter_type == 'U' || filter_type == 'u')
kalman_filter.prediction_step(dt, yaw_rate + randn()*0.05);
end
measurement = [mx,my];
watch_point = 4500;
watch_point_x = watch_point + measurement(1);
watch_point_y = watch_point + measurement(2);
measurement_history_x = [measurement_history_x,measurement(1)];
measurement_history_y = [measurement_history_y,measurement(2)];
vehicle_position_history_x = [vehicle_position_history_x,vehicle_model.x_pos];
vehicle_position_history_y = [vehicle_position_history_y,vehicle_model.y_pos];
if (filter_type == 'L' || filter_type == 'l')
vehicle_velocity_history_x = [vehicle_velocity_history_x,vehicle_model.x_vel];
vehicle_velocity_history_y = [vehicle_velocity_history_y,vehicle_model.y_vel];
end
if (filter_type == 'E' || filter_type == 'e') || (filter_type == 'U' || filter_type == 'u')
vehicle_heading_history = [vehicle_heading_history,vehicle_model.yaw];
vehicle_velocity_history = [vehicle_velocity_history,vehicle_model.vel];
end
if (filter_type == 'L' || filter_type == 'l')
kalman_filter.update_step(measurement)
end
if (filter_type == 'E' || filter_type == 'e') || (filter_type == 'U' || filter_type == 'u')
kalman_filter.update_step_linear(measurement)
if sgl > 0 && seagull == 1
dx = sgl_data_x - vehicle_model.x_pos;
dy = sgl_data_y - vehicle_model.y_pos;
polar_sight = sqrt(dx^2 + dy^2);
orientation =(atan2(dy,dx) - vehicle_model.yaw);
kalman_filter.update_step(sgl_data_x, sgl_data_y, polar_sight, orientation, sgl_range_std , sgl_heading_std);
end
end
estimated_state_history_x_pos = [estimated_state_history_x_pos,kalman_filter.state(1)];
estimated_state_history_y_pos = [estimated_state_history_y_pos,kalman_filter.state(2)];
if (filter_type == 'L' || filter_type == 'l')
estimated_state_history_x_vel = [estimated_state_history_x_vel,kalman_filter.state(3)];
estimated_state_history_y_vel = [estimated_state_history_y_vel,kalman_filter.state(4)];
end
if (filter_type == 'E' || filter_type == 'e') || (filter_type == 'U' || filter_type == 'u')
estimated_state_history_yaw = [estimated_state_history_yaw,kalman_filter.state(3)];
estimated_state_history_vel = [estimated_state_history_vel,kalman_filter.state(4)];
end
estimated_covariance_history_x_pos = [estimated_covariance_history_x_pos,kalman_filter.covariance(1,1)];
estimated_covariance_history_y_pos = [estimated_covariance_history_y_pos,kalman_filter.covariance(2,2)];
if (filter_type == 'L' || filter_type == 'l')
estimated_covariance_history_x_vel = [estimated_covariance_history_x_vel,kalman_filter.covariance(3,3)];
estimated_covariance_history_y_vel = [estimated_covariance_history_y_vel,kalman_filter.covariance(4,4)];
end
if (filter_type == 'E' || filter_type == 'e') || (filter_type == 'U' || filter_type == 'u')
estimated_covariance_history_yaw = [estimated_covariance_history_yaw,kalman_filter.covariance(3,3)];
estimated_covariance_history_vel = [estimated_covariance_history_vel,kalman_filter.covariance(4,4)];
end
A = chaser_model.x_pos;
B = vehicle_model.x_pos;
C = chaser_model.y_pos;
D = vehicle_model.y_pos;
if (filter_type == 'L' || filter_type == 'l')
estimation_error = [kalman_filter.state(1) - vehicle_model.x_pos,...
kalman_filter.state(2) - vehicle_model.y_pos,...
kalman_filter.state(3) - vehicle_model.x_vel,...
kalman_filter.state(4) - vehicle_model.y_vel];
estimation_error_history = [estimation_error_history,estimation_error];
chaser_error = [chaser_model.x_pos - kalman_filter.state(1),...
chaser_model.y_pos - kalman_filter.state(2),...
chaser_model.x_vel - kalman_filter.state(3),...
chaser_model.y_vel - kalman_filter.state(4)];
chaser_error_history = [chaser_error_history,chaser_error];
Gain = dlqr(kalman_filter.F, kalman_filter.G, 99*eye(4), 0.99 * eye(2));
input_acc = - Gain * chaser_error';
chaser_model.update_vehicle(dt,input_acc(1),input_acc(2),0);
end
if (filter_type == 'E' || filter_type == 'e') || (filter_type == 'U' || filter_type == 'u')
estimation_error = [kalman_filter.state(1) - vehicle_model.x_pos,...
kalman_filter.state(2) - vehicle_model.y_pos,...
kalman_filter.state(3) - vehicle_model.yaw,...
kalman_filter.state(4) - vehicle_model.vel];
estimation_error_history = [estimation_error_history,estimation_error];
chaser_error = [chaser_model.x_pos - kalman_filter.state(1),...
chaser_model.y_pos - kalman_filter.state(2),...
chaser_model.x_vel - kalman_filter.state(4) * cos(kalman_filter.state(3)),...
chaser_model.y_vel - kalman_filter.state(4) * sin(kalman_filter.state(3))];
F = [1 0 dt 0;
0 1 0 dt;
0 0 1 0;
0 0 0 1];
G = [0.5*dt*dt, 0;
0, 0.5*dt*dt;
dt 0;
0 dt];
% Gain = dlqr(F, G, 9999*eye(4), 0.99 * eye(2));
Gain = place(F,G,[0.1 0.2 0.25 0.15]/100);
input_acc = - Gain * chaser_error';
chaser_model.update_vehicle(dt,input_acc(1),input_acc(2),0);
end
chaser_position_history_x = [chaser_position_history_x,chaser_model.x_pos];
chaser_position_history_y = [chaser_position_history_y,chaser_model.y_pos];
chaser_velocity_history_x = [chaser_velocity_history_x,chaser_model.x_vel];
chaser_velocity_history_y = [chaser_velocity_history_y,chaser_model.y_vel];
chaser_acceleration_history_x = [chaser_acceleration_history_x,input_acc(1)];
chaser_acceleration_history_y = [chaser_acceleration_history_y,input_acc(2)];
for j = 1:length(Shark)
Shark(j).XData = Xs{j} + chaser_model.x_pos;
Shark(j).YData = Ys{j} + chaser_model.y_pos;
end
if ~isempty(kalman_filter.innovation)
innovation_history_x = [innovation_history_x,kalman_filter.innovation(1)];
innovation_history_y = [innovation_history_y,kalman_filter.innovation(2)];
end
if norm([A C] - [B D]) < 200
plot(mx,my,'.r','MarkerSize',40)
blood = blood + 1;
else
plot(mx,my,'c+','linew',5)
end
pause(0.1)
if strcmpi(motion_type,'random') == 1
if strcmpi(run_mode,'cmdrive') == 1
if (vehicle_model.x_pos > min(axis) && vehicle_model.x_pos < max(axis) &&...
vehicle_model.y_pos > min(axis) && vehicle_model.y_pos < max(axis))
vehicle_model.yaw = deg2rad(kappa) + Roll + bias_Roll;
end
if rad2deg(Pitch) > 20
kappa = 0;
end
if rad2deg(Pitch) < -20
kappa = 180;
end
end
if strcmpi(run_mode,'simu') == 1
if (vehicle_model.x_pos > min(axis) && vehicle_model.x_pos < max(axis) &&...
vehicle_model.y_pos > min(axis) && vehicle_model.y_pos < max(axis))
vehicle_model.yaw = vehicle_model.yaw + rand() - 1/2;
end
end
end
if (blood == max_blood_units)
delete(findobj('type', 'patch'));
txt = 'Done';
text(1000,1000,txt,'FontSize',20,'Color','w')
try
w = waitforbuttonpress;
if(w == 1)
close all
break;
else
close all
break;
end
catch
end
delete(elapsed_time)
et = num2str(counter_seconds);
elapsed_time = annotation('textbox', [0, 1, 0, 0], 'string', et,'FontSize',20,'Color','w');
end
counter_seconds = counter_seconds + 0.25;
if mod(i,2) == 1 && floor(counter_seconds) == counter_seconds
et = num2str(counter_seconds);
elapsed_time = annotation('textbox', [0, 1, 0, 0], 'string', et,'FontSize',20,'Color','w');
end
if (filter_type == 'E' || filter_type == 'e') || (filter_type == 'U' || filter_type == 'u')
if seagull == 1
sgl = sgl + 1;
if draw_sgl == 1
right_wing_x = [0 0.5 1] * 500 - watch_point_x;
right_wing_y = [0 0.5 0] * 500 - watch_point_y;
left_wing_x = [-1 -0.5 0] * 500 - watch_point_x;
left_wing_y = [0 0.5 0] * 500 - watch_point_y;
sgl_data_x = right_wing_x(1);
sgl_data_y = right_wing_y(1);
draw_sgl = 0;
for j = 1:length(Seagull)
Seagull(j).YData = Yl{j} + sgl_data_y;
Seagull(j).XData = Xl{j} + sgl_data_x;
end
end
vision(sgl) = plot([right_wing_x(3),vehicle_model.x_pos],[right_wing_y(3), vehicle_model.y_pos],'y--','linew',2.5);
if mod(sgl,2) == 0
if exist('vision')
set(vision,'Visible','off')
end
end
end
end
if mod(i,2) == 0
delete(elapsed_time)
end
end
i = i + 1;
end
close all
innovation_covariance_history_x = ones(length(innovation_history_x),1) * kalman_filter.innovation_covariance(1,1);
innovation_covariance_history_y = ones(length(innovation_history_y),1) * kalman_filter.innovation_covariance(2,2);
while 1
prompt = 'Proceed to analyze filter performance? [Y/N] ';
str = input(prompt,'s');
if (str == 'Y' || str == 'y')
fig = figure('NumberTitle', 'off', 'Name', 'Innovation Analyzer');
if size(MP,1)>1
pos=get(fig,'Position');
set(fig,'Position',[pos(1,2)+MP(2,1:2) pos(3:4)]);
end
if size(MP,1) == 1
else
newPosition(1) = newPosition(1) + MP(N,1);
end
fig.set('Position', newPosition, 'units', 'normalized');
fig.WindowState = 'maximized';
a01 = subplot(2,2,1);
plot(innovation_history_x / 100,'linew',1.5,'color',[0.560000 0.930000 0.560000])
hold on
plot([0, length(innovation_history_x)],[0,0],'r--','linew',1.5)
plot(1*sqrt(innovation_covariance_history_x),'--','linew',1.5,'color',[0.980000 0.840000 0.650000])
plot(-1*sqrt(innovation_covariance_history_x),'--','linew',1.5,'color',[0.980000 0.840000 0.650000])
axis tight
grid on
a03 = subplot(2,2,3);
plot(innovation_history_y / 100,'linew',1.5,'color',[0.560000 0.930000 0.560000])
hold on
plot([0, length(innovation_history_y)],[0,0],'r--','linew',1.5)
plot(1*sqrt(innovation_covariance_history_y) ,'--','linew',1.5,'color',[0.980000 0.840000 0.650000])
plot(-1*sqrt(innovation_covariance_history_y),'--','linew',1.5,'color',[0.980000 0.840000 0.650000])
axis tight
grid on
a02 = subplot(2,2,2);
h1 = histfit(chaser_acceleration_history_x/100); alpha(0.5);
h1(2).Color = 'b';
hold on
histfit(vehicle_acceleration_history_x); alpha(0.5);
axis off
axis tight
a04 = subplot(2,2,4);
h2 = histfit(chaser_acceleration_history_y/100); alpha(0.5);
h2(2).Color = 'b';
hold on
histfit(vehicle_acceleration_history_y); alpha(0.5);
axis off
axis tight
legend(a01,'inovatie','nul (0)','varianta','location','westoutside','fontsize',14)
legend(a04,'comanda','urmaritor','acceleratie','proces','location','eastoutside','fontsize',14)
break;
end
if (str == 'N' || str == 'n')
break;
end
if (str ~= 'N' && str ~= 'Y' && str ~= 'n' && str ~= 'y')
disp('Not a viable choice')
break;
end
end
if (str == 'Y' || str == 'y')
fig = figure('NumberTitle', 'off', 'Name', 'Performance Analyzer');
if size(MP,1)>1
pos=get(fig,'Position');
set(fig,'Position',[pos(1,2)+MP(2,1:2) pos(3:4)]);
end
if size(MP,1) == 1
else
newPosition(1) = newPosition(1) + MP(N,1);
end
fig.set('Position', newPosition, 'units', 'normalized');
fig.WindowState = 'maximized';
ax1 = subplot(4,2,1);
plot(vehicle_position_history_x,'color',[0 0.4470 0.7410],'linew',1.5)
hold on
plot(chaser_position_history_x,'color',[0.9290 0.6940 0.1250],'linew',1.5)
plot(estimated_state_history_x_pos,'color',[0.4660 0.6740 0.1880],'linew',1.5)
plot(measurement_history_x,'x','color',[0.200000 0.080000 0.080000],'Markersize',10)
title('Pozitie X');
grid on;
axis tight
ax1.Position = ax1.Position + [0 0 0.05 0.05];
ax2 = subplot(4,2,2);
plot(vehicle_position_history_y,'color',[0 0.4470 0.7410],'linew',1.5)
hold on
plot(chaser_position_history_y,'color',[0.9290 0.6940 0.1250],'linew',1.5)
plot(estimated_state_history_y_pos,'color',[0.4660 0.6740 0.1880],'linew',1.5)
plot(measurement_history_y,'x','color',[0.200000 0.080000 0.080000],'Markersize',10)
title('Pozitie Y');
grid on;
axis tight
ax2.Position = ax2.Position + [0 0 0.05 0.05];
if (filter_type == 'L' || filter_type == 'l')
ax3 = subplot(4,2,3);
plot(vehicle_velocity_history_x / 100 ,'color',[0 0.4470 0.7410],'linew',1.5)
hold on
plot(chaser_velocity_history_x / 100,'color',[0.9290 0.6940 0.1250],'linew',1.5)
plot((estimated_state_history_x_vel) / 100 ,'color',[0.4660 0.6740 0.1880],'linew',1.5)
title('Viteza X');
grid on;
axis tight
ax3.Position = ax3.Position + [0 -0.05 0.05 0.05];
ax4 = subplot(4,2,4);
plot((vehicle_velocity_history_y / 100) ,'color',[0 0.4470 0.7410],'linew',1.5)
hold on
plot(chaser_velocity_history_y / 100,'color',[0.9290 0.6940 0.1250],'linew',1.5)
plot((estimated_state_history_y_vel) / 100 ,'color',[0.4660 0.6740 0.1880],'linew',1.5)
title('Viteza Y')
grid on;
axis tight
ax4.Position = ax4.Position + [0 -0.05 0.05 0.05];
end
if (filter_type == 'E' || filter_type == 'e') || (filter_type == 'U' || filter_type == 'u')
ax3 = subplot(4,2,3);
plot(wrapTo180(rad2deg(vehicle_heading_history)),'color',[0 0.4470 0.7410],'linew',1.5)
hold on
plot(wrapTo180(rad2deg(estimated_state_history_yaw)) ,'color',[0.4660 0.6740 0.1880],'linew',1.5)
title('Orientare');
grid on;
axis tight