%UKF_PREDICT1  Nonaugmented (Additive) UKF prediction step%% Syntax:%   [M,P] = UKF_PREDICT1(M,P,[a,Q,param,alpha,beta,kappa,mat])%% In:%   M - Nx1 mean state estimate of previous step%   P - NxN state covariance of previous step%   a - Dynamic model function as a matrix A defining%       linear function a(x) = A*x, inline function,%       function handle or name of function in%       form a(x,param)                   (optional, default eye())%   Q - Process noise of discrete model   (optional, default zero)%   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%% Description:%   Perform additive form Unscented Kalman Filter prediction step.%%   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_UPDATE1, UKF_PREDICT2, UKF_UPDATE2, UKF_PREDICT3, UKF_UPDATE3,%   UT_TRANSFORM, UT_WEIGHTS, UT_MWEIGHTS, UT_SIGMAS% Copyright (C) 2003-2006 Simo S鋜kk