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ekf_predict2

PURPOSE ^

EKF_PREDICT2 2nd order Extended Kalman Filter prediction step

SYNOPSIS ^

function [M,P] = ekf_predict2(M,P,A,F,Q,a,W,param)

DESCRIPTION ^

EKF_PREDICT2  2nd order Extended Kalman Filter prediction step

 Syntax:
   [M,P] = EKF_PREDICT2(M,P,[A,F,Q,a,W,param])

 In:
   M - Nx1 mean state estimate of previous step
   P - NxN state covariance of previous step
   A - Derivative of a() with respect to state as
       matrix, inline function, function handle or
       name of function in form A(x,param)                 (optional, default identity)
   F - NxNxN Hessian matrix of the state transition function
       w.r.t. state variables as matrix, inline
       function, function handle or name of function
       in form F(x,param)                                  (optional, default identity)
   Q - Process noise of discrete model                     (optional, default zero)
   a - Mean prediction E[a(x[k-1],q=0)] as vector,
       inline function, function handle or name
       of function in form a(x,param)                      (optional, default A(x)*X)
   W - Derivative of a() with respect to noise q
       as matrix, inline function, function handle
       or name of function in form W(x,k-1,param)          (optional, default identity)
   param - Parameters of a                                 (optional, default empty)

   

 Out:
   M - Updated state mean
   P - Updated state covariance
   
 Description:
   Perform Extended Kalman Filter prediction step.

 See also:
   EKF_PREDICT1, EKF_UPDATE1, EKF_UPDATE2, DER_CHECK, LTI_DISC,
   KF_PREDICT, KF_UPDATE

CROSS-REFERENCE INFORMATION ^

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