📄 urts_smooth1.m
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%URTS_SMOOTH1 Additive form Unscented Rauch-Tung-Striebel smoother%% Syntax:% [M,P,D] = URTS_SMOOTH1(M,P,a,Q,[param,alpha,beta,kappa,mat,same_p])%% In:% M - NxK matrix of K mean estimates from Unscented Kalman filter% P - NxNxK matrix of K state covariances from Unscented Kalman Filter% a - Dynamic model function as a matrix A defining% linear function a(x) = A*x, inline function,% function handle or name of function in% form a(x,param) (optional, default eye())% Q - NxN process noise covariance matrix or NxNxK matrix% of K state process noise covariance matrices for each step.% param - Parameters of a. Parameters should be a single cell array,% vector or a matrix containing the same parameters for each% step, or if different parameters are used on each step they% must be a cell array of the format { param_1, param_2, ...},% where param_x contains the parameters for step x as a cell array,% a vector or a matrix. (optional, default empty)% alpha - Transformation parameter (optional)% beta - Transformation parameter (optional)% kappa - Transformation parameter (optional)% mat - If 1 uses matrix form (optional, default 0)% same_p - If 1 uses the same parameters % on every time step (optional, default 1) %% Out:% M - Smoothed state mean sequence% P - Smoothed state covariance sequence% D - Smoother gain sequence% % Description:% Unscented Rauch-Tung-Striebel smoother algorithm. Calculate% "smoothed" sequence from given Kalman filter output sequence by% conditioning all steps to all measurements.%% Example:% m = m0;% P = P0;% MM = zeros(size(m,1),size(Y,2));% PP = zeros(size(m,1),size(m,1),size(Y,2));% for k=1:size(Y,2)% [m,P] = ukf_predict1(m,P,a,Q);% [m,P] = ukf_update1(m,P,Y(:,k),h,R);% MM(:,k) = m;% PP(:,:,k) = P;% end% [SM,SP] = urts_smooth(MM,PP,a,Q);%% See also:% URTS_SMOOTH2, UKF_PREDICT1, UKF_UPDATE1, UKF_PREDICT2, UKF_UPDATE2,% UKF_PREDICT3, UKF_UPDATE3, UT_TRANSFORM, UT_WEIGHTS, UT_MWEIGHTS,% UT_SIGMAS% Copyright (C) 2006 Simo S鋜kk
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