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eimm_update

PURPOSE ^

IMM_UPDATE Interacting Multiple Model (IMM) Filter update step

SYNOPSIS ^

function [X_i,P_i,MU,X,P] = eimm_update(X_p,P_p,c_j,ind,dims,Y,H,h,R,param)

DESCRIPTION ^

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

CROSS-REFERENCE INFORMATION ^

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