ekf_predict2.m

documentation for optimal filtering toolbox for mathematical software package Matlab. The methods i

%EKF_PREDICT2  2nd order Extended Kalman Filter prediction step
%
% Syntax:
%   [M,P] = EKF_PREDICT2(M,P,[A,F,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 identity)
%   F - NxNxN Hessian matrix of the state transition function
%       w.r.t. state variables as matrix, inline
%       function, function handle or name of function
%       in form F(x,param)                                  (optional, default identity)
%   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,k-1,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_PREDICT1, EKF_UPDATE1, EKF_UPDATE2, DER_CHECK, LTI_DISC, 
%   KF_PREDICT, KF_UPDATE

% History:
%   22.5.07  JH Initial version. Modified from ekf_predict1.m
%            originally created by SS.
%
% Copyright (C) 2007 Jouni Hartikainen, Simo S鋜kk