Fast iterative algorithm for harmonics retrieval

A fast iterative algorithm for high-resolution harmonic retrieval is presented. By using a projection matrix, the prediction matrix equation is recast into a form where the iterative method can be applied. This form ensures that the iterative process converges to a unique minimum-norm least squares solution. The conjugate gradient method is used to speed-up the rate of convergence of the iterative process. No singular value decompositions of a data matrix or eigenvalue decompositions of a covariance matrix are needed. Furthermore, no prior information regarding the number of signals is required. It is shown that by incorporating known information about the signals into the iterative process, the algorithm will perform better than the MFBLP method of Tufts and Kumarsian (1982)