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ukf_predict3

PURPOSE ^

UKF_PREDICT3 Augmented (state, process and measurement noise) UKF prediction step

SYNOPSIS ^

function [M,P,X,w,C] = ukf_predict3(M,P,f,Q,R,f_param,alpha,beta,kappa,mat)

DESCRIPTION ^

UKF_PREDICT3  Augmented (state, process and measurement noise) UKF prediction step

 Syntax:
   [M,P,X,w] = UKF_PREDICT3(M,P,f,Q,R,f_param,alpha,beta,kappa)

 In:
   M - Nx1 mean state estimate of previous step
   P - NxN state covariance of previous step
   f - Dynamic model function as inline function,
       function handle or name of function in
       form a([x;w],param)
   Q - Non-singular covariance of process noise w
   R - Measurement covariance.
   f_param - Parameters of f               (optional, default empty)
   alpha - Transformation parameter      (optional)
   beta  - Transformation parameter      (optional)
   kappa - Transformation parameter      (optional)
   mat   - If 1 uses matrix form         (optional, default 0)

 Out:
   M - Updated state mean
   P - Updated state covariance
   X - Sigma points of x
   w - Weights as cell array {mean-weights,cov-weights,c}
 
 Description:
   Perform augmented form Unscented Kalman Filter prediction step
   for model

    x[k+1] = a(x[k],w[k],param)

   Function a should be such that it can be given
   DxN matrix of N sigma Dx1 points and it returns 
   the corresponding predictions for each sigma
   point. 

 See also:
   UKF_PREDICT1, UKF_UPDATE1, UKF_PREDICT2, UKF_UPDATE2, UKF_UPDATE3
   UT_TRANSFORM, UT_WEIGHTS, UT_MWEIGHTS, UT_SIGMAS

CROSS-REFERENCE INFORMATION ^

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