New Efficient LS and SVD Based Techniques for High-Resolution Frequency Estimation

New least squares and singular value decomposition based methods for the estimation of the frequencies of complex sinusoids in white noise are presented. The methods are based on a new symmetric prediction problem that has some very useful properties leading to algorithms that have considerably reduced complexity. The new symmetric predictor is superior in performance as compared to the well known symmetric Smoother and has a performance comparable to other well known methods. Finally a new LS based method, which combines the new prediction technique with the FBLP method is proposed. This method performs slightly better than the FBLP offering at the same time a significant computational saving. As a by-product in the derivation of the new methods is the establishment of some useful properties concerning the eigenstructure of Hermitian and Persymmetric matrices.