代码搜索：EKF
找到约 475 项符合「EKF」的源代码
代码结果 475
www.eeworm.com/read/382866/8994374
h ekf.h
// EKF.h: interface for the EKF class.
//
//////////////////////////////////////////////////////////////////////
#if !defined(AFX_EKF_H__9ACAA5CD_95F2_491B_932B_8F3FD28E0001__INCLUDED_)
#define
www.eeworm.com/read/382866/8994379
cpp ekf.cpp
// EKF.cpp: implementation of the EKF class.
//
//////////////////////////////////////////////////////////////////////
//#include "stdafx.h"
//#include "MultiSensorFusion.h"
#include "EKF.h"
www.eeworm.com/read/381705/9076475
m ekf.m
clear all;
clc;
det=1;
q=1e-5;
r=0.1;
PNC=0.01;
A=[1 0 det 0 ;
0 1 0 det ;
0 0 1 0 ;
0 0 0 1 ];
Q=[0 0 0 0 ;
0 0 0 0 ;
0 0 1e-4 0 ;
0 0 0 1e-4];
C=[r 0;
0 PN
www.eeworm.com/read/381017/9115218
m ekf.m
function [xhat_data,Pmat]=ekf(kalmfilex,kalmfiley,linfile,xbar,...
P0,q,r,u,y,timeidx,optpar)
% EKF
% This function is an implementation of the conventional
% extended Kalman fil
www.eeworm.com/read/183070/9179555
m ekf.m
function [xhat_data,Pmat]=ekf(kalmfilex,kalmfiley,linfile,xbar,...
P0,q,r,u,y,timeidx,optpar)
% EKF
% This function is an implementation of the conventional
% extended Kalman fil
www.eeworm.com/read/365849/9843883
m ekf.m
function obj=ekf(varargin)
% Constructor for the EKF (Extended Kalman Filter) filter
%
% Syntax: (* = optional)
%
% ekfobj = ekf(model, t*);
% ekfobj = ekf(simobj);
%
% In arguments:
%
% 1.
www.eeworm.com/read/164759/10089903
m ekf.m
% %主程序，返回
%
% %
% % %-----------------------------------------------------------------------------------------------
function [Z2]=EKF;
%丛Ini.m中调用所有用到的参数
[XTrue,Z,Z0,T,Q,DeltaR,DeltaS
www.eeworm.com/read/419623/10853259
m ekf.m
function [xhat_data,Pmat]=ekf(kalmfilex,kalmfiley,linfile,xbar,...
P0,q,r,u,y,timeidx,optpar)
% EKF
% This function is an implementation of the conventional
% extended Kalman filter
www.eeworm.com/read/418881/10893286
asv ekf.asv
function fdekf
[B,F,G,X,Xt]=track1;
P=[0.005 0 0 0;0 0.005 0 0;0 0 0.005 0;0 0 0 0.005];
X1(:,1)=[200 1.3 1500 -0.3];
for i=1:999
X1(:,i+1)=F*X1(:,i);
H=[-X1(3,i+1)/(X1(3,i
www.eeworm.com/read/272822/10943007
m ekf.m
%参考文献
%Decoupling joint probabilistic data association algorithm for multiple target tracking
%杂波环境下多传感器的数据融合
%三维常速CV模型
clear all;
clc;
T=1;