%UTF_SMOOTH1  Smoother based on two unscented Kalman filters%% Syntax:%   [M,P] = UTF_SMOOTH1(M,P,Y,[ia,Q,aparam,h,R,hparam,,alpha,beta,kappa,mat,same_p_a,same_p_h])%% In:%   M - NxK matrix of K mean estimates from Kalman filter%   P - NxNxK matrix of K state covariances from Kalman Filter%   Y - Measurement vector%  ia - Inverse prediction as a matrix IA defining%       linear function ia(xw) = IA*xw, inline function,%       function handle or name of function in%       form ia(xw,param)                         (optional, default eye())%   Q - Process noise of discrete model           (optional, default zero)%   aparam - Parameters of a                      (optional, default empty)%   h  - Measurement model function as a matrix H defining%        linear function h(x) = H*x, inline function,%        function handle or name of function in%        form h(x,param)%   R  - Measurement noise covariance.%   hparam - Parameters of h              (optional, default aparam)%   alpha - Transformation parameter      (optional)%   beta  - Transformation parameter      (optional)%   kappa - Transformation parameter      (optional)%   mat   - If 1 uses matrix form         (optional, default 0)%   same_p_a - If 1 uses the same parameters %              on every time step for a   (optional, default 1)%   same_p_h - If 1 uses the same parameters %              on every time step for h   (optional, default 1) %% Out:%   M - Smoothed state mean sequence%   P - Smoothed state covariance sequence%   % Description:%   Two filter nonlinear smoother algorithm. Calculate "smoothed"%   sequence from given extended Kalman filter output sequence%   by conditioning all steps to all measurements.%% Example:%   [...]%% See also:%   UKF_PREDICT1, UKF_UPDATE1% History:%   02.08.2007 JH Changed the name to utf_smooth1%   04.05.2007 JH Added the possibility to pass different parameters for a and h%                 for each step.%   2006       SS Initial version.           % Copyright (C) 2006 Simo S鋜kk