ekf_update2.m

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

%EKF_UPDATE2  2nd order Extended Kalman Filter update step
%
% Syntax:
%   [M,P,K,MU,S,LH] = EKF_UPDATE2(M,P,Y,H,H_xx,R,[h,V,param])
%
% In:
%   M  - Nx1 mean state estimate after prediction step
%   P  - NxN state covariance after prediction step
%   Y  - Dx1 measurement vector.
%   H  - Derivative of h() with respect to state as matrix,
%        inline function, function handle or name
%        of function in form H(x,param)
%   H_xx - DxNxN Hessian of h() with respect to state as matrix,
%          inline function, function handle or name of function
%          in form H_xx(x,param) 
%   R  - Measurement noise covariance.
%   h  - Mean prediction (measurement model) as vector,
%        inline function, function handle or name
%        of function in form h(x,param).                 (optional, default H(x)*X)
%   V  - Derivative of h() with respect to noise as matrix,
%        inline function, function handle or name
%        of function in form V(x,param).                 (optional, default identity)
%   param - Parameters of h                              (optional, default empty)
%
% Out:
%   M  - Updated state mean
%   P  - Updated state covariance
%   K  - Computed Kalman gain
%   MU - Predictive mean of Y
%   S  - Predictive covariance Y
%   LH - Predictive probability (likelihood) of measurement.
%   
% Description:
%   Extended Kalman Filter measurement update step.
%   EKF model is
%
%     y[k] = h(x[k],r),   r ~ N(0,R)
%
% See also:
%   EKF_PREDICT1, EKF_UPDATE1, EKF_PREDICT2, DER_CHECK, LTI_DISC, 
%   KF_UPDATE, KF_PREDICT

% Copyright (C) 2002-2006 Simo S鋜kk