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ckf_predict

PURPOSE ^

CKF_PREDICT - Cubature Kalman filter prediction step

SYNOPSIS ^

function [M,P] = ckf_predict(M,P,f,Q,f_param)

DESCRIPTION ^

 CKF_PREDICT - Cubature Kalman filter prediction step

 Syntax:
   [M,P] = CKF_PREDICT(M,P,[f,Q,f_param])

 In:
   M - Nx1 mean state estimate of previous step
   P - NxN state covariance of previous step
   f - Dynamic model function as a matrix A defining
       linear function f(x) = A*x, inline function,
       function handle or name of function in
       form f(x,param)                   (optional, default eye())
   Q - Process noise of discrete model   (optional, default zero)
   f_param - Parameters of f               (optional, default empty)

 Out:
   M - Updated state mean
   P - Updated state covariance

 Description:
   Perform additive form spherical-radial cubature Kalman filter (CKF)
   prediction step.

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

 See also:
   CKF_UPDATE, CRTS_SMOOTH, CKF_TRANSFORM, SPHERICALRADIAL

 References:
   Arasaratnam and Haykin (2009). Cubature Kalman Filters.
    IEEE Transactions on Automatic Control, vol. 54, no. 5, pp.1254-1269

CROSS-REFERENCE INFORMATION ^

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