Accurate frequency estimation using SVD method and Steiglitz-McBride algorithm

In many applications it is desired to estimate frequencies of sinusoids from a signal which is contaminated by a white noise. The Steiglitz-McBride algorithm (S-MA) works well for one sinusoid or when frequencies are seperated widely. However, when frequencies are close, the S-MA does not work. In the S-MA the initial estimate of the characteristic equation coefficients is based on Prony´s method which is biased. The singular value decomposition (SVD) method works much better than Prony´s method. The SVD method is used for initial estimate of frequencies and then the S-MA is used to estimate a single frequency at a time while all other frequencies are held fixed in each iteration. The iteration continues until the solution converges. It is shown that the new method improves the performance greatly