function [M,P] = ghkf_predict(M,P,f,Q,f_param,p)
% GHKF_PREDICT - Gauss-Hermite Kalman filter prediction step
%
% Syntax:
% [M,P] = GHKF_PREDICT(M,P,[f,Q,f_param,p])
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as a matrix A defining
% linear function f(x) = A*x, inline function,
% function handle or name of function in
% form f(x,param) (optional, default eye())
% Q - Process noise of discrete model (optional, default zero)
% f_param - Parameters of f (optional, default empty)
% p - Degree of approximation (number of quadrature points)
%
% Out:
% M - Updated state mean
% P - Updated state covariance
%
% Description:
% Perform additive form Gauss-Hermite Kalman Filter prediction step.
%
% Function f(.) should be such that it can be given a
% DxN matrix of N sigma Dx1 points and it returns
% the corresponding predictions for each sigma
% point.
%
% See also:
% GHKF_UPDATE, GHRTS_SMOOTH, GH_TRANSFORM
% History:
% Aug 5, 2010 - Renamed from 'gh_predict' to 'ghkf_predict' (asolin)
% Copyright (C) 2009 Hartikainen, Särkkä, Solin
%
% $Id: gh_predict.m,v 1.2 2009/07/01 06:34:40 ssarkka Exp $
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
%%
%
% Check which arguments are there
%
if nargin < 2
error('Too few arguments');
end
if nargin < 3
f = [];
end
if nargin < 4
Q = [];
end
if nargin < 5
f_param = [];
end
if nargin < 6
p = [];
end
%
% Apply defaults
%
if isempty(f)
f = eye(size(M,1));
end
if isempty(Q)
Q = zeros(size(M,1));
end
if isempty(p)
p = 10;
end
%
% Do transform and add process noise
%
tr_param = {p};
[M,P] = gh_transform(M,P,f,f_param,tr_param);
P = P + Q;