Kalman Filter For Beginners With Matlab Examples Download

K = P_pred / (P_pred + measurement_noise)

To make this practical, we have prepared a downloadable ZIP file containing:

👉 Download link: (Placeholder – on your website, replace with actual link)
[Download Kalman Filter for Beginners – MATLAB Examples (.zip, 4 KB)] kalman filter for beginners with matlab examples download

No login required. Just unzip and run in MATLAB R2020b or newer. K = P_pred / (P_pred + measurement_noise)

You have just built a 1D Kalman filter. Now challenge yourself: To make this practical, we have prepared a

Recommended free resources:

This example estimates 1D position and velocity using noisy position measurements.

Code (save as kalman_demo.m):

% kalman_demo.m - simple 2D constant-velocity Kalman filter
dt = 0.1;            % time step
T = 20;              % total time (s)
N = T/dt;
% System matrices
A = [1 dt; 0 1];     % state transition (position, velocity)
B = [0; 0];          % no control
H = [1 0];           % measure position only
% Noise covariances
sigma_process_pos = 0.01;
sigma_process_vel = 0.1;
Q = diag([sigma_process_pos^2, sigma_process_vel^2]);  % process noise
R = 1.0;            % measurement noise variance
% True state and measurements
x_true = [0; 1];    % start at 0 m with 1 m/s
X_true = zeros(2,N);
Z = zeros(1,N);
for k=1:N
    % propagate true state with small process noise
    w = mvnrnd([0;0], Q)';
    x_true = A*x_true + w;
    X_true(:,k) = x_true;
    z = H*x_true + sqrt(R)*randn;
    Z(k) = z;
end
% Kalman filter initialization
x_est = [0; 0];     % initial estimate
P = eye(2);         % initial covariance
X_est = zeros(2,N);
for k=1:N
    % Predict
    x_pred = A * x_est;
    P_pred = A * P * A' + Q;
% Update
    S = H * P_pred * H' + R;
    K = P_pred * H' / S;
    z = Z(k);
    x_est = x_pred + K * (z - H * x_pred);
    P = (eye(2) - K * H) * P_pred;
X_est(:,k) = x_est;
end
% Plot results
time = (0:N-1)*dt;
figure;
subplot(2,1,1);
plot(time, X_true(1,:), 'g-', time, X_est(1,:), 'b--', time, Z, 'rx');
legend('True position','Estimated position','Measurements');
xlabel('Time (s)'); ylabel('Position');
title('Kalman Filter: Position');
subplot(2,1,2);
plot(time, X_true(2,:), 'g-', time, X_est(2,:), 'b--');
legend('True velocity','Estimated velocity');
xlabel('Time (s)'); ylabel('Velocity');
title('Kalman Filter: Velocity');