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

- gauss_pdf GAUSS_PDF Multivariate Gaussian PDF

- imm_filter IMM_FILTER Interacting Multiple Model (IMM) Filter prediction and update steps

Generated on Fri 12-Aug-2011 15:08:47 by