IMMPredict:

Path: Common/Estimation

% Interactive Multiple Models prediction step.
 This does the interaction and filter steps. The inputs and outputs only show
 data structure fields used in this function. This assumes that the number and
 definitions of the state do not change. There are n models and m states.
 IMMUpdate is normally called after IMMPredict.

 A complete IMM implementation is:

   IMMPredict( d )
   IMMUpdate( d )

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   Form:
   d = IMMPredict( d )
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   ------
   Inputs
   ------
   d	  (.)  IMM data structure
              .use     (1,1)   'KF' 'EKF' 'UKF' Only KF is implemented
              .pIJ     (n,n)   Model transition probability
              .muI     (1,n)   Probability of being in that model
              .mI      (m,n)   Means for each model
              .pI      {n}     Covariance matrix for each model
              .aI      {n}     State transition matrix for each model
              .qI      {n}     Plant noise matrix for each model

   -------
   Outputs
   -------
   d   (.)  IMM data structure
              .cHatJ	  (1,n)	Scaling factor
              .mPI     (m,n)   Mean vectors for each model
              .pPI     {n}     Covariance matrix for each model

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Children:

Common: Estimation/KFPredict

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