armorfrepeat.M

function varargout = armorfrepeat(x,Nr,Nl,p)
%ARMORF   AR parameter estimation via LWR method by Morf modified.
%   X is a matrix whose every row is one variable's time series
%   Nr is the number of realizations, Nl is the length of every realization
%   If the time series are stationary long, just let Nr=1, Nl=(1,length(X))
%
%   A = ARMORF(X,NR,NL,ORDER) returns the polynomial coefficients A corresponding to 
%   the AR model estimate of matrix X using Morf's method.
%   ORDER is the order of the AR model.
%
%   [A,E] = ARMORF(...) returns the final prediction error E (the variance
%   estimate of the white noise input to the AR model).
%
%   [A,E,K] = ARMORF(...) returns the vector K of reflection 
%   coefficients (parcor coefficients).
%
%   Ref: M. Morf, etal, Recursive Multichannel Maximum Entropy Spectral Estimation,
%              IEEE trans. GeoSci. Elec., 1978, Vol.GE-16, No.2, pp85-94.
%        S. Haykin, Nonlinear Methods of Spectral Analysis, 2nd Ed.
%              Springer-Verlag, 1983, Chapter 2
%
%   finished on Aug.9, 2002 by Yonghong Chen

% Initialization
[L,N]=size(x);
R0=zeros(L,L);
R0f=R0;
R0b=R0;
pf=R0;
pb=R0;
pfb=R0;
ap(:,:,1)=R0;
bp(:,:,1)=R0;
En=R0;
counting = 0; % lq
for i = 1 : Nr
    En = En + x(:,Nl(i,1):Nl(i,2)) * x(:,Nl(i,1):Nl(i,2))';    % lq
    ap(:,:,1) = ap(:,:,1) + x(:,Nl(i,1)+1:Nl(i,2)) * x(:,Nl(i,1)+1:Nl(i,2))';      % lq
    bp(:,:,1) = bp(:,:,1) + x(:,Nl(i,1):Nl(i,2)-1) * x(:,Nl(i,1):Nl(i,2)-1)';% lq
    counting = counting + Nl(i,2) - Nl(i,1);% lq
end
ap(:,:,1) = inv((chol(ap(:,:,1)/counting))');% lq
bp(:,:,1) = inv((chol(bp(:,:,1)/counting))');% lq
for i=1:Nr
    efp = ap(:,:,1)*x(:,Nl(i,1)+1:Nl(i,2));           % lq    
    ebp = bp(:,:,1)*x(:,Nl(i,1):Nl(i,2)-1); % lq
    pf = pf + efp*efp';
    pb = pb + ebp*ebp';
    pfb = pfb + efp*ebp';
end
En = chol(En/N)'; % Covariance of the noise

% Initial output variables
coeff = [];% eye(L); % Coefficient matrices of the AR model
kr=[];  % reflection coefficients

for m=1:p
   % Calculate the next order reflection (parcor) coefficient
   ck = inv((chol(pf))')*pfb*inv(chol(pb));
   kr=[kr,ck];
   % Update the forward and backward prediction errors
   ef = eye(L)- ck*ck';
   eb = eye(L)- ck'*ck;
     
   % Update the prediction error
   En = En*chol(ef)';
   E = (ef+eb)./2;   
   
   % Update the coefficients of the forward and backward prediction errors
   ap(:,:,m+1) = zeros(L);
   bp(:,:,m+1) = zeros(L);
   pf = zeros(L);
   pb = zeros(L);
   pfb = zeros(L);

   for i=1:m+1       
       a(:,:,i) = inv((chol(ef))')*(ap(:,:,i)-ck*bp(:,:,m+2-i));
       b(:,:,i) = inv((chol(eb))')*(bp(:,:,i)-ck'*ap(:,:,m+2-i));
   end
   for k=1:Nr
       efp = zeros(L,Nl(k,2)-Nl(k,1)+1-m-1);
       ebp = zeros(L,Nl(k,2)-Nl(k,1)+1-m-1);
       for i=1:m+1
           k1=m+2-i+Nl(k,1);
           k2=Nl(k,2)-Nl(k,1)+1-i+ Nl(k,1);
           efp = efp+a(:,:,i)*x(:,k1:k2);
           ebp = ebp+b(:,:,m+2-i)*x(:,k1-1:k2-1);
       end
       pf = pf + efp*efp';
       pb = pb + ebp*ebp';
       pfb = pfb + efp*ebp';
   end
   ap = a;
   bp = b;
end

for j=1:p
    coeff = [coeff,inv(a(:,:,1))*a(:,:,j+1)];
end

varargout{1} = coeff;
if nargout >= 2
    varargout{2} = En*En';
end
if nargout >= 3
    varargout{3} = kr;
end