%UKF_UPDATE2 - Augmented form Unscented Kalman Filter update step%% Syntax:%   [M,P,K,IM,IS,LH] = UKF_UPDATE3(M,P,Y,h,R,X,w,param,alpha,beta,kappa,mat,sigmas)%% In:%   M  - Mean state estimate after prediction step%   P  - State covariance after prediction step%   Y  - Measurement vector.%   h  - Measurement model function as a matrix H defining%        linear function h(x) = H*x+r, inline function,%        function handle or name of function in%        form h([x;r],param)%   R  - Measurement covariance.%   X - Sigma points of x%   w - Weights as cell array {mean-weights,cov-weights,c}%   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%   K  - Computed Kalman gain%   MU - Predictive mean of Y%   S  - Predictive covariance Y%   LH - Predictive probability (likelihood) of measurement.%   % Description:%   Perform augmented form Discrete Unscented Kalman Filter (UKF)%   measurement update step. Assumes additive measurement%   noise.%%   Function h should be such that it can be given%   DxN matrix of N sigma Dx1 points and it returns %   the corresponding measurements for each sigma%   point. This function should also make sure that%   the returned sigma points are compatible such that%   there are no 2pi jumps in angles etc.%% Example:%   h = inline('atan2(x(2,:)-s(2),x(1,:)-s(1))','x','s');%   [M2,P2] = ukf_update2(M1,P1,Y,h,R,S);%% See also:%   UKF_PREDICT1, UKF_UPDATE1, UKF_PREDICT2, UKF_UPDATE2, UKF_PREDICT3%   UT_TRANSFORM, UT_WEIGHTS, UT_MWEIGHTS, UT_SIGMAS%% History:%   04.05.2007 JH Initial version. Modified from ukf_update1.m%              originally created by SS.%   % % References:%   [1] Wan, Merwe: The Unscented Kalman Filter%% Copyright (C) 2007 Jouni Hartikainen, Simo S鋜kk