www.pudn.com > gmm_utilities.zip > KF_update_w_simple.m

function [x,P,w]= KF_update_w_simple(x,P,v,R,H, logflag) 
%function [x,P,w]= KF_update_w_simple(x,P,v,R,H, logflag) 
% 
% Calculate the Kalman Filter update given the prior state [x,P], the innovation, v, the  
% observe uncertainty R, and the (linearised) observation model H. The weight, w, is the 
% update normalising constant.  
% 
% This simple implementation is to check the validity of KF_update_w(). 
% 
% Tim Bailey 2005. 
 
S = H*P*H' + R;  % innovation covariance 
W = P*H'*inv(S); % Kalman gain 
 
x = x + W*v; 
P = P - W*S*W'; 
w = gauss_likelihood(v,S,logflag);