%KF_LHOOD Kalman Filter measurement likelihood
%
% Syntax:
% LH = KF_LHOOD(X,P,Y,H,R)
%
% In:
% X - Nx1 state mean
% P - NxN state covariance
% Y - Dx1 measurement vector.
% H - Measurement matrix.
% R - Measurement noise covariance.
%
% Out:
% LH - Likelihood of measurement.
%
% Description:
% Calculate likelihood of measurement in Kalman filter.
% If and X and P define the parameters of predictive
% distribution (e.g. from KF_PREDICT)
%
% p(x[k] | y[1:k-1]) = N(x[k] | m-[k], P-[k])
%
% then this likelihood is the probability of measurement
% in innovation distribution:
%
% p(y[k] | y[1:k-1]) = N(y[k] | IM, IS)
%
% See also:
% KF_PREDICT, KF_UPDATE
% History:
% 20.11.2002 The first official version.
%
% Copyright (C) 2002 Simo Särkkä
%
% $Id: kf_lhood.m 111 2007-09-04 12:09:23Z ssarkka $
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function LH = kf_lhood(m,P,y,H,R)
%
% Check which arguments are there
%
if nargin < 5
error('Too few arguments');
end
IM = H*m;
IS = (H*P*H'+R);
LH = gauss_pdf(y,IM,IS);