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A unified approach to three eigendecomposition-based methods for frequency estimation in the presence of noise is presented. These are: the Tufts-Kumaresan (TK) method, the Minimum-Norm (MN) method and the Total Least Squares ( T U ) method. It is shown that: 1) The MN method is a modified version of the TK method; 2) The TLS method is a generalization of the MN method; 3) The TLS solutionvector can be expressed in matrix form and an alternate way of computing it is presented; 4) The MN methods exhibit some improvement over the TK method.

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