www.gusucode.com > 用粒子滤波算法进行跟踪的matlab代码 > gmm_utilities/gmm_entropy.m
function p = gmm_entropy(g,N) g = gmm_normalise(g); if nargin == 2 p = gmm_entropy_montecarlo(g, N); else p = gmm_entropy_unscented(g); end % % function p = gmm_entropy_montecarlo(g, N) % Monte Carlo approximation of entropy for Gaussian mixtures. s = gmm_samples(g, N); w = gmm_evaluate(g, s); p = -mean(log(w(w~=0))); % % function p = gmm_entropy_unscented(g) % Unscented gmm entropy, based on Goldberger's KLD approximation [D,N] = size(g.x); Ds = sqrt(D); p = 0; for i=1:N Ps = Ds * matrix_square_root(g.P(:,:,i), 1); x = repvec(g.x(:,i), D); s = [x+Ps, x-Ps]; % unscented samples for i-th component of g w = gmm_evaluate(g, s); p = p - g.w(i)*sum(log(w)); end p = p/(2*D); % % function R = matrix_square_root(P, type) switch type case 1 % svd decomposition, P = U*D*U' = R*R' (UDU form is also called modified Cholesky decomposition) [U,D,V] = svd(P); R = U*sqrt(D); case 2 % cholesky decomposition (triangular), P = R*R' R = chol(P)'; case 3 % principal square root (symmetric), P = R*R R = sqrtm(P); end