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