gusucode.com > 用粒子滤波算法进行跟踪的matlab代码 > gmm_utilities/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);