  In this paper, we address the problem of parameter estimation of a 2-D chirp
model under the assumption that the errors are stationary. We extend the 2-D
periodogram method for the sinusoidal model, to find initial values to use in
any iterative procedure to compute the least squares estimators (LSEs) of the
unknown parameters, to the 2-D chirp model. Next we propose an estimator, known
as the approximate least squares estimator (ALSE), that is obtained by
maximising a periodogram-type function and is observed to be asymptotically
equivalent to the LSE. Moreover the asymptotic properties of these estimators
are obtained under slightly mild conditions than those required for the LSEs.
For the multiple component 2-D chirp model, we propose a sequential method of
estimation of the ALSEs, that significantly reduces the computational
difficulty involved in reckoning the LSEs and the ALSEs. We perform some
simulation studies to see how the proposed method works and a data set has been
analysed for illustrative purposes.
