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%Kalman filter %x=Ax+B(u+w(k)); %y=Cx+D+v(k) clear all; close all; ts=0.001; M=3000; %Continuous Plant a=25;b=133; sys=tf(b,[1,a,0]); dsys=c2d(sys,ts,'z'); [num,den]=tfdata(dsys,'v');

% CLOSEPSAT clear all PSAT global variables from workspace % % CLOSEPSAT % % Author: Federico Milano % Date: 22-Feb-2004 % Version: 1.0.0 % % E-mail: fmilano@thunderbox.uwaterloo.ca % Web

function [AltErr, VelErr, BallErr] = HybridBody % Hybrid extended Kalman filter example. % Track a body falling through the atmosphere. % Outputs are: % AltErr = RMS altitude estimation error

function Correlated(M, MFilter) % Kalman filter simulation using correlated process and measurement noise. % This illustrates the improvement in filter results that can be attained % when the cor

function KalmanConstrained % function KalmanConstrained % This m-file simulates a vehicle tracking problem. % The vehicle state is estimated with a Kalman filter. % In addition, with the a prior

function DiscreteKFEx1(N) % Discrete time Kalman filter for position estimation of a Newtonian system. % This example illustrates the effectiveness of the Kalman filter for state % estimation. It

/******************************************************************************/ /* */ /* MEASURE.H: struct type and e

%Discrete Kalman filter for PID control %Reference kalman_2rank.m %x=Ax+B(u+w(k)); %y=Cx+D+v(k) clear all; close all; ts=0.001; %Continuous Plant a=25;b=133; sys=tf(b,[1,a,0]); dsys=c2d(sy

%Kalman filter %x=Ax+B(u+w(k)); %y=Cx+D+v(k) clear all; close all; ts=0.001; M=3000; %Continuous Plant a=25;b=133; sys=tf(b,[1,a,0]); dsys=c2d(sys,ts,'z'); [num,den]=tfdata(dsys,'v');

%Discrete Kalman filter %x=Ax+B(u+w(k)); %y=Cx+D+v(k) function [u]=kalman(u1,u2,u3) persistent A B C D Q R P x yv=u2; if u3==0 x=zeros(2,1); ts=0.001; a=25;b=133; sys=tf(b,[1,a