% --- Plot Results --- figure; plot(t, true_pos, 'g-', 'LineWidth', 2); hold on; plot(t, measurements, 'r.', 'MarkerSize', 6); plot(t, x_est, 'b-', 'LineWidth', 2); legend('True Position', 'Noisy Measurements', 'Kalman Filter Estimate'); xlabel('Time (seconds)'); ylabel('Position (meters)'); title('Kalman Filter: Tracking a Moving Object (Phil Kim Style)'); grid on;
: Instead of storing massive amounts of historical data (batch processing), recursive filters update their current estimate using only the previous state and the newest measurement. This makes them efficient for real-time systems like drones or digital scales. The Two-Step Cycle :
: A classic tracking problem where you only measure position but want to know how fast the object is moving. % --- Plot Results --- figure; plot(t, true_pos,
: Simple starting points for understanding how to smooth out noisy voltage or sonar data.
That’s it. The scary matrix version is just this same logic applied to multiple dimensions. : Simple starting points for understanding how to
% Update estimate using measurement x_est(k) = x_pred + K * (measurements(k) - x_pred);
: For systems that don't follow straight lines—like a robot turning—the book introduces the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) . Implementing a Basic Filter in MATLAB Kalman Filter for Beginners: With MATLAB Examples % Update estimate using measurement x_est(k) = x_pred
The Kalman Filter alternates between (Guess) and Update (Measure).