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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

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] = 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] = 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 [xhat, P] = Kalman(xhat, y, sigmaX, sigmaY, P) % function [xhat, P] = Kalman(xhat, y, sigmaX, sigmaY, P) % % Compute the state estimate. % INPUTS % xhat = present state estimate 当

function [EstAve, MeasAve] = KalmanTS(sigmaX, sigmaY, fOptimal) % function [EstAve, MeasAve] = KalmanTS(sigmaX, sigmaY, fOptimal) % % Monte-Carlo simulation of the truck-trailer system. % INPUTS

function [x, y, xhat] = ModelLinear(x, u, xhat, sigmaX, sigmaY) % function [x, y, xhat] = ModelLinear(x, u, xhat, sigmaX, sigmaY) % % Simulate the linear model of the truck-trailer system. % INP

Model { Name "halfwave" Version 6.1 MdlSubVersion 0 GraphicalInterface { NumRootInports 0 NumRootOutports 0 ParameterArgumentNames "" ComputedModelVe

Model { Name "boost" Version 6.1 MdlSubVersion 0 GraphicalInterface { NumRootInports 0 NumRootOutports 0 ParameterArgumentNames "" ComputedModelVersi

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