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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]=ekf(fstate,x,P,hmeas,z,Q,R) % EKF Extended Kalman Filter for nonlinear dynamic systems % [x, P] = ekf(f,x,P,h,z,Q,R) returns state estimate, x and state covariance, P % for nonlin

n=3; %number of state q=0.1; %std of process r=0.1; %std of measurement Q=q^2*eye(n); % covariance of process R=r^2; % covariance of measurement f=@(x)[x(2);x(3);0.05*x(1)*(x

//-----------------------------------------------------------------------------------// // Windows Graphics Programming: Win32 GDI and DirectDraw // //

k=3; t=0.5; a0=[1 1 1 1 1 1]'; p0=eye(6); mes_total=6; measurement=[1 2 3 4 5 6;1 2 3 4 5 6]; r=eye(2); yz2=2.5;

% LR_3d_KALMAN.M calculate the log likelihood ratio using % standard discrete-time Kalman filter for the following system: % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

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

DscQMMMeasureFrequency - Universal Driver Documentation