Beginners With Matlab Examples Download Top [best] | Kalman Filter For

% Update the state estimate and covariance matrix innovation = z(i) - H * x_pred; S = H * P_pred * H' + R; K = P_pred * H' / S; x_est(i) = x_pred + K * innovation; P_est(i) = P_pred - K * H * P_pred; end

% State Vector: x = [position; velocity] x = [0; 0]; % Initial guess (we assume it starts at 0,0 - this is wrong on purpose to test the filter) % Update the state estimate and covariance matrix

Used for non-linear systems (like tracking a turning car). velocity] x = [0

A Kalman filter is an optimal mathematical algorithm that estimates the true state of a system from a series of incomplete and noisy measurements. It works in real-time by combining a theoretical prediction with actual sensor data to find the most accurate state. % Update the state estimate and covariance matrix