Home > src > kf_lhood.m

kf_lhood

PURPOSE ^

KF_LHOOD Kalman Filter measurement likelihood

SYNOPSIS ^

function LH = kf_lhood(m,P,y,H,R)

DESCRIPTION ^

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

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:
Generated on Fri 12-Aug-2011 15:08:47 by m2html © 2005