utf_smooth1.m

用EKF/UKF的Matlab仿真程序包

%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