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Measurement 的代码
kf_update.m
%KF_UPDATE Kalman Filter update step
%
% Syntax:
% [X,P,K,IM,IS,LH] = KF_UPDATE(X,P,Y,H,R)
%
% In:
% X - Nx1 mean state estimate after prediction step
% P - NxN state covariance after predictio
bot_d2h_dx2.m
% Hessian of the measurement function in BOT-demo.
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer t
bot_d2h_dx2.m
% Hessian of the measurement function in BOT-demo.
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer t
kf_update.m
%KF_UPDATE Kalman Filter update step
%
% Syntax:
% [X,P,K,IM,IS,LH] = KF_UPDATE(X,P,Y,H,R)
%
% In:
% X - Nx1 mean state estimate after prediction step
% P - NxN state covariance after predictio
bot_d2h_dx2.m
% Hessian of the measurement function in BOT-demo.
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer t
dlqew.m
function [l,m,p,e] = dlqew(a,g,c,j,q,r)
%DLQEW Discrete linear quadratic estimator design for the system:
% x[n+1] = Ax[n] + Bu[n] + Gw[n] {State equation}
% z[n] = Cx[n] + Du[n] + Jw
modelnonlinear.m
function [x, y, xhat] = ModelNonlinear(x, u, xhat, sigmaX, sigmaY)
% function [x, y, xhat] = ModelNonlinear(x, u, xhat, sigmaX, sigmaY)
%
% Simulate the nonlinear model of the truck-trailer syste
rfc1242.txt
Network Working Group S. Bradner, Editor
Request for Comments: 1242 Harvard University
kf_update.m
%KF_UPDATE Kalman Filter update step
%
% Syntax:
% [X,P,K,IM,IS,LH] = KF_UPDATE(X,P,Y,H,R)
%
% In:
% X - Nx1 mean state estimate after prediction step
% P - NxN state covariance after predictio