%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% History:%   %   02.8.2007 JH Changed the name to tf_smooth%   26.3.2007 JH Fixed a bug in backward filter with observations having%                having more than one dimension.%             % Copyright (C) 2006 Simo S鋜kk