  In this paper, we propose some estimation techniques to estimate the
elementary chirp model parameters, which are encountered in sonar, radar,
acoustics, and other areas. We derive asymptotic theoretical properties of
least squares estimators and approximate least squares estimators for the
one-component elementary chirp model. It is proved that the proposed estimators
are strongly consistent and follow the normal distribution asymptotically. We
also suggest how to obtain proper initial values for these methods. The problem
of finding initial values is a difficult problem when the number of components
in the model is large, or when the signal-to-noise ratio is low, or when two
frequency rates are close to each other. We propose sequential procedures to
estimate the multiple-component elementary chirp model parameters. We prove
that the theoretical properties of sequential least squares estimators and
sequential approximate least squares estimators coincide with those of least
squares estimators and approximate least squares estimators, respectively. To
evaluate the performance of the proposed estimators, numerical experiments are
performed. It is observed that the proposed sequential estimators perform well
even in situations where least squares estimators do not perform well. We
illustrate the performance of the proposed sequential algorithm on a bat data.
