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Measurement 的代码
ukf_bshfun.m
%/upf_demos/ukf_bshfun.m
function [y] = ukf_bshfun(x,u,n,t);
% PURPOSE : Measurement model function for UKF
% INPUTS : - x: The evaluation point in the domain.
% : - u: exogenous inputs
ukf_update1.m
%UKF_UPDATE1 - Additive form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,IM,S,LH] = UKF_UPDATE1(M,P,Y,h,R,param,alpha,beta,kappa,mat)
%
% In:
% M - Mean state estimate after predict
ukf_update3.m
%UKF_UPDATE2 - Augmented form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,IM,IS,LH] = UKF_UPDATE3(M,P,Y,h,R,X,w,param,alpha,beta,kappa,mat,sigmas)
%
% In:
% M - Mean state estimate a
ukf_update2.m
%UKF_UPDATE2 - Augmented form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,IM,IS,LH] = UKF_UPDATE2(M,P,Y,h,R,param,alpha,beta,kappa,mat)
%
% In:
% M - Mean state estimate after predic
test_linear_analytic_measurement_model_gaussian_uncertainty.cpp
// Test Program
// Tests the Linear Measurement Model with Gaussian uncertainty (additive)
// Measuring the (vertical) distance to a wall
#include
kalman.c
/*
Tracking of rotating point.
Rotation speed is constant.
Both state and measurements vectors are 1D (a point angle),
Measurement is the real point angle + gaussian noise.
The re
kalman.c
/*
Tracking of rotating point.
Rotation speed is constant.
Both state and measurements vectors are 1D (a point angle),
Measurement is the real point angle + gaussian noise.
The re
kalman.c
/*
Tracking of rotating point.
Rotation speed is constant.
Both state and measurements vectors are 1D (a point angle),
Measurement is the real point angle + gaussian noise.
The re