tells the filter whether to trust the model or the sensor more.
The beauty? The Kalman gain is calculated dynamically at each step using statistics (covariance matrices). The filter "learns" which source to trust based on the noise levels you provide. kalman filter for beginners with matlab examples download
I hope this helps! Let me know if you have any questions or need further clarification. tells the filter whether to trust the model
% --- Calculate RMS Error --- pos_error_kf = sqrt(mean((x_hist(1,:) - x_true(1,:)).^2)); pos_error_meas = sqrt(mean((measurements - x_true(1,:)).^2)); fprintf('RMS Position Error:\n'); fprintf(' Raw Measurements: %.3f m\n', pos_error_meas); fprintf(' Kalman Filter: %.3f m\n', pos_error_kf); fprintf('Improvement: %.1f%%\n', (1 - pos_error_kf/pos_error_meas)*100); The filter "learns" which source to trust based
Instead of simple subtraction, you use matrix multiplication (
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If the noise is Gaussian, the Kalman filter provides the best possible estimate.