Parameter Estimation of Multicomponent 2D Polynomial-Phase Signals Using the 2D PHAF-Based Approach

This paper considers parameter estimation of multicomponent 2D polynomial-phase signals (mc-2D PPSs). The main contributions included are: Derivation of the Cramer-Rao lower bound (CRLB) for mc-2D PPSs; Two dimensional product high-order ambiguity function (2D PHAF) based estimation procedure, recently proposed by Barbarossa, for the estimation of third-order mc-2D PPSs is extended to signals of arbitrary orders; Procedure for optimal lag set selection in the proposed 2D PHAF has been proposed; Since the 2D PHAF is characterized by large mean squared error (MSE) for higher-order signals, refinement procedure is introduced that is able to improve the estimation accuracy and to reduce MSE up to the CRLB. The proposed estimator has been used for target focusing in radar images.