IMM_UPDATE Interacting Multiple Model (IMM) Filter update step Syntax: [X_i,P_i,MU,X,P] = IMM_UPDATE(X_p,P_p,c_j,ind,dims,Y,H,h,R,param) In: X_p - Cell array containing N^j x 1 mean state estimate vector for each model j after prediction step P_p - Cell array containing N^j x N^j state covariance matrix for each model j after prediction step c_j - Normalizing factors for mixing probabilities ind - Indices of state components for each model as a cell array dims - Total number of different state components in the combined system Y - Dx1 measurement vector. 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 R - Measurement noise covariances for each model as a cell array. param - Parameters of h Out: X_i - Updated state mean estimate for each model as a cell array P_i - Updated state covariance estimate for each model as a cell array MU - Estimated probabilities of each model X - Combined updated state mean estimate P - Combined updated covariance estimate Description: IMM-EKF filter measurement update step. If some of the models have linear measurements standard Kalman filter update step is used for those. See also: IMM_PREDICT, IMM_SMOOTH, IMM_FILTER

- ekf_update1 EKF_UPDATE1 1st order Extended Kalman Filter update step

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