<p>One dimensional (1-D) and two dimensional (2-D) chirp models have received a considerable amount of attention in the Statistical Signal Processing literature. Recently, Grover (<a href="https://etd.iitk.ac.in:8443/jspui/handle/123456789/18684">https://etd.iitk.ac.in:8443/jspui/handle/123456789/18684</a>, 2019) proposed a one dimensional chirp-like model that behaves similarly to the classical chirp model but can be implemented more conveniently. It is observed that the signals generated by the two models are virtually indistinguishable. In this paper we propose a 2-D chirp-like model, and it is observed that it behaves very similar to a 2-D chirp model. We propose a least squares method to estimate the unknown parameters and derive the asymptotic properties of the least squares estimators. It is observed that the computation of the least squares estimators involves solving high-dimensional optimization problem, and it can be quite demanding if the number of components is large. In this paper we have proposed to use the sequential least squares estimators which can be implemented quite conveniently. We derive the asymptotic properties of the sequential least squares estimators, and the convergence rates of the sequential least squares estimators are same as those of the least squares estimators. The asymptotic variances of both the least squares estimators and the sequential least squares estimators attain the Cramer-Rao lower bound under the assumptions of Gaussian errors. Extensive simulations have been performed to show the effectiveness of the proposed methods. We have provided two illustrative examples to show how the proposed method can be used in practice.</p>