Extracting the fundamental frequency of a nonlinear chirp signal with modulated harmonic structure using ML, target tracking, and the Viterbi algorithm

Abstract

We address the problem of extracting a time-varying fundamental frequency from a signal which has multiple, possibly aliased, harmonics, observed in potentially very high noise. The approach consists of an ML detector employing compressed likelihoods, followed by one of two processing stages which filter out unreasonable detections: either a target tracking approach or a Viterbi algorithm. Results show very good ability to extract the fundamental, even in very noisy data.