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Chirp signal models and their generalizations have been used to model manynatural and man-made phenomena in signal processing and time series literature.In recent times, several methods have been proposed for parameter estimation ofthese models. These methods however are either statistically sub-optimal orcomputationally burdensome, specially for two dimensional (2D) chirp models. Inthis paper, we consider the problem of parameter estimation of 2D chirp modelsand propose a computationally efficient estimator and establish asymptotictheoretical properties of the proposed estimators. And the proposed estimatorsare observed to have the same rates of convergence as the least squaresestimators (LSEs). Furthermore, the proposed estimators of chirp rateparameters are shown to be asymptotically optimal. Extensive and detailednumerical simulations are conducted, which support theoretical results of theproposed estimators.

A Computationally Efficient algorithm to estimate the Parameters of a Two-Dimensional Chirp Model with the product term