%EKF_PREDICT1  1st order Extended Kalman Filter prediction step
%
% Syntax:
%   [M,P] = EKF_PREDICT1(M,P,[A,Q,a,W,param])
%
% In:
%   M - Nx1 mean state estimate of previous step
%   P - NxN state covariance of previous step
%   A - Derivative of a() with respect to state as
%       matrix, inline function, function handle or
%       name of function in form A(x,param)       (optional, default eye())
%   Q - Process noise of discrete model               (optional, default zero)
%   a - Mean prediction E[a(x[k-1],q=0)] as vector,
%       inline function, function handle or name
%       of function in form a(x,param)                (optional, default A(x)*X)
%   W - Derivative of a() with respect to noise q
%       as matrix, inline function, function handle
%       or name of function in form W(x,param)        (optional, default identity)
%   param - Parameters of a                           (optional, default empty)
%
% Out:
%   M - Updated state mean
%   P - Updated state covariance
%   
% Description:
%   Perform Extended Kalman Filter prediction step.
%
% See also:
%   EKF_UPDATE1, EKF_PREDICT2, EKF_UPDATE2, DER_CHECK,
%   LTI_DISC, KF_PREDICT, KF_UPDATE

% Copyright (C) 2002-2006 Simo S鋜kk