代码搜索：predict
找到约 2,271 项符合「predict」的源代码
代码结果 2,271
www.eeworm.com/read/407295/11422543
bak kf_predict.m.bak
%KF_PREDICT Perform Kalman Filter prediction step
%
% Syntax:
% [X,P] = KF_PREDICT(X,P,A,Q,B,U)
%
% In:
% X - Nx1 mean state estimate of previous step
% P - NxN state covariance of previ
www.eeworm.com/read/407295/11422553
m ekf_predict1.m
%EKF_PREDICT1 1st order Extended Kalman Filter prediction step
%
% Syntax:
% [M,P] = EKF_PREDICT1(M,P,[A,Q,a,W,param])
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN st
www.eeworm.com/read/407295/11422554
m ukf_predict3.m
%UKF_PREDICT3 Augmented (state, process and measurement noise) UKF prediction step
%
% Syntax:
% [M,P,X,w] = UKF_PREDICT3(M,P,a,Q,R,[param,alpha,beta,kappa])
%
% In:
% M - Nx1 mean state estimate
www.eeworm.com/read/400947/11566694
asv f_initiate_predict.asv
function [z_rt, s_rt]=f_initiate_predict(x_current,p_current);
%新航迹下一时刻的预测
model_try;%给模型参数赋值
hh=[eye(9),eye(9),eye(9),eye(9),eye(9),eye(9)]';%计算I阵
pp=[p_current(:,1:9),zeros(9),zeros(9),zeros(9
www.eeworm.com/read/400947/11566755
m f_initiate_predict.m
function [z_rt, s_rt]=f_initiate_predict(x_current,p_current);
%新航迹下一时刻的预测
model_try;%给模型参数赋值
hh=[eye(9),eye(9),eye(9),eye(9),eye(9),eye(9)]';%计算I阵
pp=[p_current(:,1:9),zeros(9),zeros(9),zeros(9
www.eeworm.com/read/251528/12339483
m ekf_predict2.m
%EKF_PREDICT2 2nd order Extended Kalman Filter prediction step
%
% Syntax:
% [M,P] = EKF_PREDICT2(M,P,[A,F,Q,a,W,param])
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state c
www.eeworm.com/read/251528/12339486
m ukf_predict1.m
%UKF_PREDICT1 Nonaugmented (Additive) UKF prediction step
%
% Syntax:
% [M,P] = UKF_PREDICT1(M,P,[a,Q,param,alpha,beta,kappa,mat])
%
% In:
% M - Nx1 mean state estimate of previous step
% P - N
www.eeworm.com/read/251528/12339490
m ukf_predict2.m
%UKF_PREDICT2 Augmented (state and process noise) UKF prediction step
%
% Syntax:
% [M,P] = UKF_PREDICT2(M,P,a,Q,[param,alpha,beta,kappa])
%
% In:
% M - Nx1 mean state estimate of previous step
%
www.eeworm.com/read/251528/12339609
m ekf_predict1.m
%EKF_PREDICT1 1st order Extended Kalman Filter prediction step
%
% Syntax:
% [M,P] = EKF_PREDICT1(M,P,[A,Q,a,W,param])
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN st
www.eeworm.com/read/251528/12339612
m ukf_predict3.m
%UKF_PREDICT3 Augmented (state, process and measurement noise) UKF prediction step
%
% Syntax:
% [M,P,X,w] = UKF_PREDICT3(M,P,a,Q,R,[param,alpha,beta,kappa])
%
% In:
% M - Nx1 mean state estimate