TF_SMOOTH Two filter based Smoother Syntax: [M,P] = TF_SMOOTH(M,P,Y,A,Q,H,R,[use_inf]) In: M - NxK matrix of K mean estimates from Kalman filter P - NxNxK matrix of K state covariances from Kalman Filter Y - Sequence of K measurement as DxK matrix A - NxN state transition matrix. Q - NxN process noise covariance matrix. H - DxN Measurement matrix. R - DxD Measurement noise covariance. use_inf - If information filter should be used (default 1) Out: M - Smoothed state mean sequence P - Smoothed state covariance sequence Description: Two filter linear smoother algorithm. Calculate "smoothed" sequence from given Kalman filter output sequence by conditioning all steps to all measurements. Example: m = m0; P = P0; MM = zeros(size(m,1),size(Y,2)); PP = zeros(size(m,1),size(m,1),size(Y,2)); for k=1:size(Y,2) [m,P] = kf_predict(m,P,A,Q); [m,P] = kf_update(m,P,Y(:,k),H,R); MM(:,k) = m; PP(:,:,k) = P; end [SM,SP] = tf_smooth(MM,PP,A,Q,H,R,Y); See also: KF_PREDICT, KF_UPDATE

- kf_predict KF_PREDICT Perform Kalman Filter prediction step
- kf_update KF_UPDATE Kalman Filter update step

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