Kalman Filter For Beginners With Matlab Examples !full! Download Top (GENUINE - FULL REVIEW)

% --- Storage for Results --- estimated_states = zeros(2, n);

This cycle of repeats for each new data point. The result is a constantly refined estimate that is statistically optimal , as long as the system is linear and the noise is Gaussian (the familiar bell curve). % --- Storage for Results --- estimated_states =

Determines whether to trust the prediction more or the measurement more. % Store the result

For more complex scenarios like radar tracking, a constant velocity model is used. (Position and Velocity) MATLAB Tool: trackingKF % --- Storage for Results --- estimated_states =

state_estimates(k) = x_hat; % Store the result