IMM_PREDICT Interacting Multiple Model (IMM) Filter prediction step Syntax: [X_p,P_p,c_j,X,P] = EIMM_PREDICT(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,a,param,Q) In: X_ip - Cell array containing N^j x 1 mean state estimate vector for each model j after update step of previous time step P_ip - Cell array containing N^j x N^j state covariance matrix for each model j after update step of previous time step MU_ip - Vector containing the model probabilities at previous time step p_ij - Model transition 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 - Function handles of dynamic model functions for each model as a cell array param - Parameters of a for each model as a cell array Q - Process noise matrices for each model as a cell array. Out: X_p - Predicted state mean for each model as a cell array P_p - Predicted state covariance for each model as a cell array c_j - Normalizing factors for mixing probabilities X - Combined predicted state mean estimate P - Combined predicted state covariance estimate Description: IMM-EKF filter prediction step. If some of the models have linear dynamics standard Kalman filter prediction step is used for those. See also: EIMM_UPDATE, EIMM_SMOOTH

- ekf_predict1 EKF_PREDICT1 1st order Extended Kalman Filter prediction step

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