function w=minnorm(y,n,m)%% The Root Min-Norm frequency estimator.%%  w=minnorm(y,n,m);%%      y  ->  the data vector%      n  ->  the model order%      m  ->  the order of the covariance matrix in (4.5.14)%      w  <-  the frequency estimates%% Copyright 1996 by R. Mosesy=y(:);N=length(y);                       % data length% compute the sample covariance matrixR=zeros(m,m);for i = m : N,   R=R+y(i:-1:i-m+1)*y(i:-1:i-m+1)'/N;end% to use the forward-backward approach, uncomment the next line% R=(R+fliplr(eye(m))*R.'*fliplr(eye(m)))/2;% get the eigendecomposition of R; use svd because it sorts eigenvalues[U,D,V]=svd(R);S=U(:,1:n);alpha = S(1,:)';Sbar = S(2:m,:);if norm(alpha) ~=1,    g = - Sbar * alpha / (1-alpha'*alpha);else,   error('The min-norm solution does not exist');end% find the n roots of the a polynomial that are nearest the unit circle,ra= conj(roots([1;g]));% pick the n roots that are closest to the unit circle[dumm,I]=sort(abs(abs(ra)-1));w=angle(ra(I(1:n)));