%UKF_PREDICT3  Augmented (state, process and measurement noise) UKF prediction step%% Syntax:%   [M,P,X,w] = UKF_PREDICT3(M,P,a,Q,R,[param,alpha,beta,kappa])%% In:%   M - Nx1 mean state estimate of previous step%   P - NxN state covariance of previous step%   a - 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.%   param - Parameters of a               (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 % Copyright (C) 2003-2006 Simo S鋜kk