A two-stage technique for estimating micro-Doppler signal parameters has been proposed. In the first stage, rough parameter estimations are performed by regression of instantaneous frequency estimate obtained from the short-time Fourier transform. Afterwards, rough estimates are refined in the second stage. The proposed technique has better performance with respect to current state-of-the-art algorithms, and it reaches the Cramer–Rao lower bound for sinusoidal frequency-modulated signal parameters.

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.

Shape parameter estimation procedures for generalized Gaussian distribution are considered. It is shown that the existing estimators can be divided into four groups: maximum likelihood algorithm; moment-based methods; entropy matching estimators and global convergence algorithm. Besides, properties of two recently introduced estimators of shape parameter are discussed. They are based on the combination of two procedures that use the evaluation of the fourth central moment and robust measure of kurtosis. Statistical properties of all considered estimators are investigated by means of defining their bias and variance values for samples of sizes 1000 and 4000 elements and shape parameter values ranging from 0.3 to 2.