The is an optimal estimation algorithm that calculates the state of a system (like the position or speed of a drone) by blending noisy sensor measurements with a mathematical prediction. How It Works: The Predict-Correct Cycle

The book is structured to build intuition through hands-on practice. It typically covers:

% Initialize the state estimate and covariance x_est = x0; P_est = P0;

subplot(3,1,2); plot(t, x_true(2,:), 'g-', 'LineWidth', 1.5); hold on; plot(t, x_hist(2,:), 'b-', 'LineWidth', 1.5); legend('True Velocity', 'Kalman Estimate'); ylabel('Velocity (m/s)'); grid on;

% --- Noise Covariances --- Q = [0.01 0; 0 0.01]; % Process noise covariance R = 1; % Measurement noise covariance (position noise)