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function R=cov_w(obj,t); % Returns the covariance matrix of the measurement noise, at time t. % % Syntax: (* = optional) % % R = cov_e(model, t); % % In arguments: % % 1. model % Model objec

function R=cov_w(obj,t); % Returns the covariance matrix of the measurement noise, at time t. % % Syntax: (* = optional) % % R = cov_e(model, t); % % In arguments: % % 1. model % Model objec

function R=cov_w(obj,t); % Returns the covariance matrix of the measurement noise, at time t. % % Syntax: (* = optional) % % R = cov_e(model, t); % % In arguments: % % 1. model % Model objec

function [y] = ukf_bshfun(x,u,n,t); % PURPOSE : Measurement model function for UKF % INPUTS : - x: The evaluation point in the domain. % : - u: exogenous inputs % : - n: measureme

function [y] = bshfun(x,u,t); % PURPOSE : Measurement model function. % INPUTS : - x: The evaluation point in the domain. % OUTPUTS : - y: The value of the function at x. % AUTHORS : Nando de Fre

function [y] = hfun(x,t); % PURPOSE : Measurement model function. % INPUTS : - x: The evaluation point in the domain. % OUTPUTS : - y: The value of the function at x. % AUTHORS : % DATE : if

function [y] = ukf_hfun(x,u,n,t); % PURPOSE : Measurement model function fpr UKF. % INPUTS : - x: Hidden state % : - u: control vector % - n: Measurement noise % - t

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

' {$STAMP BS2} ' ============================================================================== ' ' File...... Ex28 - DS1620.BS2 ' Purpose... Temperature measurement ' Author.... Parallax ' E-ma

function z= add_observation_noise(z,R, addnoise) %function z= add_observation_noise(z,R, addnoise) % % Add random measurement noise. We assume R is diagonal. if addnoise == 1 len= size(