  Chirp signals are quite common in many natural and man-made systems like
audio signals, sonar, radar etc. Estimation of the unknown parameters of a
signal is a fundamental problem in statistical signal processing. Recently,
Kundu and Nandi \cite{2008} studied the asymptotic properties of least squares
estimators of the unknown parameters of a simple chirp signal model under the
assumption of stationary noise. In this paper, we propose periodogram-type
estimators called the approximate least squares estimators to estimate the
unknown parameters and study the asymptotic properties of these estimators
under the same error assumptions. It is observed that the approximate least
squares estimators are strongly consistent and asymptotically equivalent to the
least squares estimators. Similar to the periodogram estimators, these
estimators can also be used as initial guesses to find the least squares
estimators of the unknown parameters. We perform some numerical simulations to
see the performance of the proposed estimators and compare them with the least
squares estimators and the estimators proposed by Lahiri et al., \cite{2013}.
We have analysed two real data sets for illustrative purposes.
