EIMM_SMOOTH EKF based fixed-interval IMM smoother using two IMM-EKF filters. Syntax: [X_S,P_S,X_IS,P_IS,MU_S] = EIMM_SMOOTH(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,a,a_param,Q,R,H,h,h_param,Y) In: MM - Means of forward-time IMM-filter on each time step PP - Covariances of forward-time IMM-filter on each time step MM_i - Model-conditional means of forward-time IMM-filter on each time step PP_i - Model-conditional covariances of forward-time IMM-filter on each time step MU - Model probabilities of forward-time IMM-filter on each time step p_ij - Model transition probability matrix ind - Indices of state components for each model as a cell array dims - Total number of different state components in the combined system A - Dynamic model matrices for each linear model and Jacobians of each non-linear model's measurement model function as a cell array a - Cell array containing function handles for dynamic functions for each model having non-linear dynamics a_param - Parameters of a as a cell array. Q - Process noise matrices for each model as a cell array. R - Measurement noise matrices for each model as a cell array. H - Measurement matrices for each linear model and Jacobians of each non-linear model's measurement model function as a cell array h - Cell array containing function handles for measurement functions for each model having non-linear measurements h_param - Parameters of h as a cell array. Y - Measurement sequence Out: X_S - Smoothed state means for each time step P_S - Smoothed state covariances for each time step X_IS - Model-conditioned smoothed state means for each time step P_IS - Model-conditioned smoothed state covariances for each time step MU_S - Smoothed model probabilities for each time step Description: EKF based two-filter fixed-interval IMM smoother. See also: EIMM_UPDATE, EIMM_PREDICT

- ekf_predict1 EKF_PREDICT1 1st order Extended Kalman Filter prediction step
- ekf_update1 EKF_UPDATE1 1st order Extended Kalman Filter update step
- gauss_pdf GAUSS_PDF Multivariate Gaussian PDF

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