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%__________________________________________________________________________ % Type: : File Header % File name : initConfig % File Description : Holds the data

% Jacobian of the measurement model function for the UNGM-model. % % Copyright (C) 2007 Jouni Hartikainen % % This software is distributed under the GNU General Public % Licence (version 2 or later);

%TF_SMOOTH Two filter based Smoother % % Syntax: % [M,P] = TF_SMOOTH(M,P,Y,A,Q,H,R,[use_inf]) % % In: % M - NxK matrix of K mean estimates from Kalman filter % P - NxNxK matrix of K state covar

function z0 = kriging(x,y,z,xi,yi) %KRIGING Summary of this function goes here % Detailed explanation goes here % STEP 1 Coefficient determination % calculate the Distance among known points for ii

%%% DynaEst 3.032 10/22/2000 % Copyright (c) 2000 Yaakov Bar-Shalom % % DoMonteCarlo, Run Monte Carlo simulations % run monte carlo simulations % Case 1 : No ExternalTruth, External Z % Case 2 :

function [x,P,K,innovation_variance] = k_updatx(x,P,A,b,R,Q); % K_UPDATX Kalman update, one measurement per call % Allows for system covariance Q % Allows for observation cova

function [x,P] = b_row(x,P,A,b,R) %B_ROW Bayes update, one measurement per call % Observation covariance R %Kai Borre 3-28-97 %Copyright (c) 1997 by Kai Borre %$Revision: 1.1 $ $Date: 19

function [x,P] = kud(x,P,H,b,var) %KUD Kalman update, one measurement per call % Observation variance: var %Kai Borre and C.C. Goad 11-24-96 %Copyright (c) by Kai Borre %$Revision: 1.0 $

function [x,P] = k_update(x,P,A,b,R) % K_UPDATE Kalman update, one measurement per call % Observation covariance R %Kai Borre 11-24-96 %Copyright (c) 1997 by Kai Borre %$Revision: 1.0

function [x,P] = k_update(x,P,A,b,R) % K_UPDATE Kalman update, one measurement per call % Observation covariance R %Kai Borre 11-24-96 %Copyright (c) 1997 by Kai Borre %$Revision: 1.0