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Variance Analysis for Least ℓ_{p}Norm Estimator in Mixture of Generalized Gaussian Noise
Yuan CHEN LongTing HUANG Xiao Long YANG Hing Cheung SO
Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Vol.E100A
No.5
pp.12261230 Publication Date: 2017/05/01 Online ISSN: 17451337
DOI: 10.1587/transfun.E100.A.1226 Type of Manuscript: LETTER Category: Digital Signal Processing Keyword: variance analysis, ℓ_{p}norm minimizer, complexvalued signal, mixture of generalized Gaussian model,
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Summary:
Variance analysis is an important research topic to assess the quality of estimators. In this paper, we analyze the performance of the least ℓ_{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 ℓ_{p}norm minimizer is first derived, for the general complexvalued 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 nearoptimality of the ℓ_{p}norm minimizer compared with CramérRao lower bound.


