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/** ################################################################### ** THIS BEAN MODULE IS GENERATED BY THE TOOL. DO NOT MODIFY IT. ** Filename : AD2.C ** Project : che_free **

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

/* ************************************************************************* * Ralink Tech Inc. * 4F, No. 2 Technology 5th Rd. * Science-based Industrial Park * Hsin-chu, Taiwan, R.O.C. * * (c)

%demonstration of particle filtering %by Paul Sundvall, KTH Signals sensors and systems 2004 clear,clc,close all %let x be the position of the boat %create a model for the movement of the boat

%demonstration of particle filtering %by Paul Sundvall, KTH Signals sensors and systems 2004 clear,clc,close all %let x be the position of the boat %create a model for the movement of the boat

% kfcov.m % Scope: This MATLAB macro generates Kalman filter covariance matrix when the % parameters are constants; conventional formulation is implemented.

% mreast.m % Scope: This MATLAB macro executes measurement reasonableness test for a % given scalar measurement. % Usage: alpha = mreast(n,ud,h,r,dz,tes

% mudm.m % Scope: This MATLAB macro implements the discrete Kalman filter measurement % updating using Bierman's U-D measurement update algorithm. Onl

% mudm1.m % Scope: This MATLAB macro implements the discrete Kalman filter measurement % updating using Bierman's U-D measurement update algorithm. On

% kfcova.m % Scope: This MATLAB macro generates Kalman filter covariance matrix when the % parameters are constants; alternate conventional formulation