Identification of linear stochastic systems via second- and fourth-order cumulant matching
IEEE Transactions on Information Theory
Digital processing of random signals: theory and methods
Digital processing of random signals: theory and methods
Matrix computations (3rd ed.)
Introduction to the generalized method of moments estimation
Generalized method of moments estimation
On Scene Segmentation and Histograms-Based Curve Evolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gain-control-free blind carrier frequency offset acquisition for QAM constellations
IEEE Transactions on Signal Processing
Blind phase recovery for QAM communication systems
IEEE Transactions on Signal Processing
Practical Issues in Estimation Over Multiaccess Fading Channels With TBMA Wireless Sensor Networks
IEEE Transactions on Signal Processing
Gain-Control-Free Near-Efficient Phase Acquisition for QAM Constellations
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Wireless Communications
Invariant Image Watermarking Based on Statistical Features in the Low-Frequency Domain
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we address the problem of location parameter estimation via a Generalized Method of Moments (GMM) approach. The general framework for the GMM estimation requires the minimization of a suitable, generally nonconvex, elliptic norm. Here we show that, if the estimandum is a shift parameter for a suitable statistic of the observations, a fast, DFT-based, computationally efficient procedure can be employed to perform the estimation. Besides we discuss the relation between the GMM estimation and the maximum likelihood (ML) estimation, showing that the GMM estimation rule provides a closed form ML estimator for shift parameters when the observations are multinomially distributed. As a case study, we analyze a GMM blind phase offset estimator for general quadrature amplitude modulation constellations. Simulation results and theoretical performance analysis show that the GMM estimator outperforms selected state of the art estimators, approaching the Cramér-Rao lower bound for a wide range of signal-to-noise ratio values.