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uimm_predict

PURPOSE ^

IMM_PREDICT UKF based Interacting Multiple Model (IMM) Filter prediction step

SYNOPSIS ^

function [X_p,P_p,c_j,X,P] = uimm_predict(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,a,param,Q)

DESCRIPTION ^

IMM_PREDICT  UKF based Interacting Multiple Model (IMM) Filter prediction step

 Syntax:
   [X_p,P_p,c_j,X,P] = UIMM_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 as a cell array
   a     - Dynamic model functions for each non-linear model
   param - Parameters of a
   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-UKF filter prediction step. If some of the models have linear
   dynamics standard Kalman filter prediction step is used for those.

 See also:
   UIMM_UPDATE, UIMM_SMOOTH

CROSS-REFERENCE INFORMATION ^

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