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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

Model { Name "puls1" Version 5.0 SaveDefaultBlockParams on SampleTimeColors off LibraryLinkDisplay "none" WideLines off ShowLineDimensions off ShowPortDataType

/* -*-C-*- ******************************************************************************** * * File: metrics.h (Formerly metrics.h) * Description: * Author: Mark Seaman, SW Product

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

%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

function [x,P] = obsup[x,P,z,H,R] % % function [x,P] = obsup[x,P,z,H,R] % % Performs Kalman filter observational update of state vector, x % and the associated covriance matrix of estimation unce

function [x,U,D] = bierman(z,R,H,xin,Uin,Din) % % Matlab implementation of the % Bierman ``square root filtering without square roots'' % % From the diskette included with % M. S. Grewal, L. R.

function [xout,Cout] = potter(z,R,H,xin,Cin) % % James H. Potter's square root filtering algorithm % for the observational update of a Cholesky factor % of the covariance matrix of state estimatio

function [x,P] = obsup[x,P,z,H,R] % % function [x,P] = obsup[x,P,z,H,R] % % Performs Kalman filter observational update of state vector, x % and the associated covriance matrix of estimation unce