Starting from the classical procedure reported by Capon for power level estimation from ML filters, the authors present how this method can be modifyed in order to obtain a power spectral density estimate. The basic idea is to compute the effective bandwidth of the ML filter, and normalize the power level estimate, at the output of a quadratic detector following the filter, with it. The effective bandwidth has been obtained by an equal area constraint criteria. Furthermore, the above mentioned estimate, we called NMLM, converges in the distributional sense to the true spectral power density. This suggest the use of new estimates, denoted in the text as q-MLM, which improves the mentioned convergence both in 1-D as well as 2-D problems of SPA.

Peer Reviewed