--- Kalman Filter For Beginners With Matlab Examples Best -

% State transition matrix F F = [1 dt; 0 1];

% Process noise covariance Q (small for constant velocity model) Q = [0.01 0; 0 0.01]; --- Kalman Filter For Beginners With MATLAB Examples BEST

%% Plot results figure('Position', [100 100 800 600]); % State transition matrix F F = [1

% Measurement: noisy GPS (standard deviation = 3 meters) measurement_noise = 3; measurements = true_pos + measurement_noise * randn(size(t)); %% Plot results figure('Position'

figure; subplot(2,1,1); plot(1:50, K_history, 'b-', 'LineWidth', 2); xlabel('Time Step'); ylabel('Kalman Gain (Position)'); title('Kalman Gain Convergence'); grid on;

x_est = x_pred + K * y; P = (eye(2) - K * H) * P_pred;

for k = 1:50 % Predict x_pred = F * x_est; P_pred = F * P * F' + Q;