کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
561142 1451945 2016 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Variance analysis of unbiased least ℓpℓp-norm estimator in non-Gaussian noise
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Variance analysis of unbiased least ℓpℓp-norm estimator in non-Gaussian noise
چکیده انگلیسی


• Theoretical performance of unbiased linear and nonlinear estimators is studied.
• Variance expressions based onℓpℓp-norm minimization in non-Gaussian noise are derived.
• To find the minimum variance under different noise types, the optimum p is computed.

Modeling time and space series in various areas of science and engineering require the values of parameters of interest to be estimated from the observed data. It is desirable to analyze the performance of estimators in an elegant manner without the need for extensive simulations and/or experiments. Among various performance measures, variance is the most basic one for unbiased estimators. In this paper, we focus on the estimator based on the ℓpℓp-norm minimization in the presence of zero-mean symmetric non-Gaussian noise. Four representative noise models, namely, αα-stable, generalized Gaussian, Student׳s tt and Gaussian mixture processes, are investigated, and the corresponding variance expressions are derived for linear and nonlinear parameter estimation problems at p≥1p≥1. The optimal choice of pp for different noise environments is studied, where the global optimality and sensitivity analyses are also provided. The developed formulas are verified by computer simulations and are compared with the Cramér-Rao lower bound.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 122, May 2016, Pages 190–203
نویسندگان
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