plot of the position and velocity rmse noise in matlab

To plot the position and velocity root mean square error (RMSE) with noise in MATLAB, you first need to generate the position and velocity data with noise. Then you can calculate the RMSE and visualize the results using MATLAB's plotting functions.

Here's an example code to get you started:


main.m
% Parameters
n = 100; % number of data points
t = linspace(0, 10, n); % time vector

% Generate position and velocity data with noise
position = sin(t) + 0.1*randn(size(t));
velocity = cos(t) + 0.1*randn(size(t));

% Calculate RMSE
rmse_position = sqrt(mean((position - sin(t)).^2));
rmse_velocity = sqrt(mean((velocity - cos(t)).^2));

% Plot the position and velocity data with noise
figure;
subplot(2,1,1);
plot(t, position, 'b', t, sin(t), 'r');
xlabel('Time');
ylabel('Position');
legend('Position with Noise', 'True Position');

subplot(2,1,2);
plot(t, velocity, 'b', t, cos(t), 'r');
xlabel('Time');
ylabel('Velocity');
legend('Velocity with Noise', 'True Velocity');

% Print RMSE values
disp(['Position RMSE: ', num2str(rmse_position)]);
disp(['Velocity RMSE: ', num2str(rmse_velocity)]);
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This code generates position and velocity data with noise and plots them against the true position and velocity. It also calculates the RMSE for both position and velocity. The resulting plots and RMSE values will be displayed in the MATLAB command window.

You can customize the code according to your specific requirements, such as changing the noise level or modifying the true position and velocity functions.

Remember to replace position and velocity with your actual position and velocity data with noise.

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