% Plot results plot(0:dt:50, true_position, 'g-', 'LineWidth', 2); hold on; plot(0:dt:50, measurements, 'rx'); plot(0:dt:50, estimated_positions, 'b--', 'LineWidth', 2); legend('True', 'Noisy GPS', 'Kalman Estimate'); xlabel('Time (s)'); ylabel('Position (m)'); title('Kalman Filter for Constant Velocity'); grid on;
State = [position; velocity; acceleration] kalman filter for beginners with matlab examples download
% Update K = P * H' / (H * P * H' + R); x = x + K * (measurements(k) - H*x); P = (eye(3) - K*H) * P; % Plot results plot(0:dt:50
estimated_positions(k) = x(1); end
% Initial state guess x = [0; 10]; % start at 0 m, velocity 10 m/s P = eye(2); % initial uncertainty title('Kalman Filter for Constant Velocity')