--- /dev/null
+function [MX,res,arg3] = durlev(AutoCov);
+% function [AR,RC,PE] = durlev(ACF);
+% function [MX,PE] = durlev(ACF);
+% estimates AR(p) model parameter by solving the
+% Yule-Walker with the Durbin-Levinson recursion
+% for multiple channels
+% INPUT:
+% ACF Autocorrelation function from lag=[0:p]
+%
+% OUTPUT
+% AR autoregressive model parameter
+% RC reflection coefficients (= -PARCOR coefficients)
+% PE remaining error variance
+% MX transformation matrix between ARP and RC (Attention: needs O(p^2) memory)
+% AR(:,K) = MX(:,K*(K-1)/2+(1:K));
+% RC(:,K) = MX(:,(1:K).*(2:K+1)/2);
+%
+% All input and output parameters are organized in rows, one row
+% corresponds to the parameters of one channel
+%
+% see also ACOVF ACORF AR2RC RC2AR LATTICE
+%
+% REFERENCES:
+% Levinson N. (1947) "The Wiener RMS(root-mean-square) error criterion in filter design and prediction." J. Math. Phys., 25, pp.261-278.
+% Durbin J. (1960) "The fitting of time series models." Rev. Int. Stat. Inst. vol 28., pp 233-244.
+% P.J. Brockwell and R. A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
+% S. Haykin "Adaptive Filter Theory" 3rd ed. Prentice Hall, 1996.
+% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
+% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
+
+% $Id: durlev.m 5090 2008-06-05 08:12:04Z schloegl $
+% Copyright (C) 1998-2002,2008 by Alois Schloegl <a.schloegl@ieee.org>
+%
+% This program is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% This program is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with this program. If not, see <http://www.gnu.org/licenses/>.
+
+
+% Inititialization
+[lr,lc]=size(AutoCov);
+
+res=[AutoCov(:,1), zeros(lr,lc-1)];
+d=zeros(lr,1);
+
+if nargout<3 % needs O(p^2) memory
+ MX=zeros(lr,lc*(lc-1)/2);
+ idx=0;
+ idx1=0;
+ % Durbin-Levinson Algorithm
+ for K=1:lc-1,
+ %idx=K*(K-1)/2; %see below
+ % for L=1:lr, d(L)=arp(L,1:K-1)*transpose(AutoCov(L,K:-1:2));end; % Matlab 4.x, Octave
+ % d=sum(MX(:,idx+(1:K-1)).*AutoCov(:,K:-1:2),2); % Matlab 5.x
+ MX(:,idx+K)=(AutoCov(:,K+1)-sum(MX(:,idx1+(1:K-1)).*AutoCov(:,K:-1:2),2))./res(:,K);
+ %rc(:,K)=arp(:,K);
+ %if K>1 %for compatibility with OCTAVE 2.0.13
+ MX(:,idx+(1:K-1))=MX(:,idx1+(1:K-1))-MX(:,(idx+K)*ones(K-1,1)).*MX(:,idx1+(K-1:-1:1));
+ %end;
+ % for L=1:lr, d(L)=MX(L,idx+(1:K))*(AutoCov(L,K+1:-1:2).');end; % Matlab 4.x, Octave
+ % d=sum(MX(:,idx+(1:K)).*AutoCov(:,K+1:-1:2),2); % Matlab 5.x
+ res(:,K+1) = res(:,K).*(1-abs(MX(:,idx+K)).^2);
+ idx1=idx;
+ idx=idx+K;
+ end;
+ %arp=MX(:,K*(K-1)/2+(1:K));
+ %rc =MX(:,(1:K).*(2:K+1)/2);
+
+else % needs O(p) memory
+
+ arp=zeros(lr,lc-1);
+ rc=zeros(lr,lc-1);
+
+ % Durbin-Levinson Algorithm
+ for K=1:lc-1,
+ % for L=1:lr, d(L)=arp(L,1:K-1)*transpose(AutoCov(L,K:-1:2));end; % Matlab 4.x, Octave
+ % d=sum(arp(:,1:K-1).*AutoCov(:,K:-1:2),2); % Matlab 5.x
+ arp(:,K) = (AutoCov(:,K+1)-sum(arp(:,1:K-1).*AutoCov(:,K:-1:2),2))./res(:,K); % Yule-Walker
+ rc(:,K) = arp(:,K);
+ %if K>1 %for compatibility with OCTAVE 2.0.13
+ arp(:,1:K-1)=arp(:,1:K-1)-arp(:,K*ones(K-1,1)).*arp(:,K-1:-1:1);
+ %end;
+ %for L=1:lr, d(L)=arp(L,1:K)*(AutoCov(L,K+1:-1:2).');end; % Matlab 4.x, Octave
+ % d=sum(arp(:,1:K).*AutoCov(:,K+1:-1:2),2); % Matlab 5.x
+ res(:,K+1) = res(:,K).*(1-abs(arp(:,K)).^2);
+ end;
+
+ % assign output arguments
+ arg3=res;
+ res=rc;
+ MX=arp;
+end; %if