Chirp signal models and their generalizations have been used to model many natural and man-made phenomena in signal processing and time series literature. In recent times, several methods have been proposed for parameter estimation of these models. These methods however are either statistically sub-optimal or computationally burdensome, specially for two dimensional (2D) chirp models. In this paper, we consider the problem of parameter estimation of 2D chirp models and propose a computationally efficient estimator and establish asymptotic theoretical properties of the proposed estimators. And the proposed estimators are observed to have the same rates of convergence as the least squares estimators (LSEs). Furthermore, the proposed estimators of chirp rate parameters are shown to be asymptotically optimal. Extensive and detailed numerical simulations are conducted, which support theoretical results of the proposed estimators.

翻译：在信号处理和时间序列文献中,已经使用奇尔普信号模型及其概括性模型来模拟许多自然和人为现象,最近提出了几种方法来估计这些模型的参数,然而,这些方法在统计上是亚最佳的,或者在计算上是累赘的,特别是两个维(2D)恰尔普模型。在本文中,我们考虑了2D恰尔普模型的参数估计问题,并提出了一个计算效率高的测算器,并确定了拟议测算器的自然和人为现象的理论特性。而且,还观察到,提议的测算器与最小正方形测算器(LSES)的趋同率相同。此外,拟议的奇尔普率参数估计器显示,在时间上是最佳的。我们进行了广泛和详细的数字模拟,这些模拟支持了拟议测算器的理论结果。