Variance Analysis for Least p-Norm Estimator in Mixture of Generalized Gaussian Noise

Yuan CHEN  Long-Ting HUANG  Xiao Long YANG  Hing Cheung SO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E100-A    No.5    pp.1226-1230
Publication Date: 2017/05/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E100.A.1226
Type of Manuscript: LETTER
Category: Digital Signal Processing
variance analysis,  p-norm minimizer,  complex-valued signal,  mixture of generalized Gaussian model,  

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Variance analysis is an important research topic to assess the quality of estimators. In this paper, we analyze the performance of the least p-norm estimator in the presence of mixture of generalized Gaussian (MGG) noise. In the case of known density parameters, the variance expression of the p-norm minimizer is first derived, for the general complex-valued signal model. Since the formula is a function of p, the optimal value of p corresponding to the minimum variance is then investigated. Simulation results show the correctness of our study and the near-optimality of the p-norm minimizer compared with Cramér-Rao lower bound.