tf_smooth.m

扩展卡尔曼和无迹卡尔曼的matlab仿真比较。

%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