Model order estimation in multi-component chirp signals

Abstract

Real datasets exhibiting non-constant frequencies, can be analyzed with the help of multi-component chirp signals adequately. This requires estimation of the number of chirp components in practice. The problem of estimation becomes difficult when the data is composed of moderate to large number of chirp components. We propose Bayesian approach to estimate the number of components of a multi-component chirp model using an approximation to the mode of the posterior distribution. This approximation is motivated from the relation between mode of the obtained posterior and the sequential least squares estimators. Extensive numerical simulations highlight superior performance of the proposed estimators than that of the estimators based on Akaike information criterion, Bayesian information criterion and asymptotic maximum a posteriori probability, for a large range of signal-to-noise ratio levels. We further illustrate the implementation of the proposed estimators through two real data examples.