An efficient methodology to estimate the parameters of a two-dimensional chirp signal model

In various capacities of statistical signal processing two-dimensional (2-D) chirp models have been considered significantly, particularly in image processing-to model gray-scale and texture images, magnetic resonance imaging, optical imaging etc. In this paper we address the problem of estimation of the unknown parameters of a 2-D chirp model under the assumption that the errors are independently and identically distributed (i.i.d.). The key attribute of the proposed estimation procedure is that it is computationally more efficient than the least squares estimation method. Moreover, the proposed estimators are observed to have the same asymptotic properties as the least squares estimators, thus providing computational effectiveness without any compromise on the efficiency of the estimators. We extend the propounded estimation method to provide a sequential procedure to estimate the unknown parameters of a 2-D chirp model with multiple components and under the assumption of i.i.d. errors we study the large sample properties of these sequential estimators. Simulation studies and two synthetic data analyses have been performed to show the effectiveness of the proposed estimators.

Automated summary

This paper focuses on the estimation of unknown parameters of a 2-D chirp model in statistical signal processing, proposing a computationally efficient method with asymptotic properties similar to least squares estimators. The proposed method can be extended to models with multiple components and is shown to be effective through simulation studies and data analyses.