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第二篇IEEE TSP论文接收

已有 1243 次阅读 2019-7-17 08:55 |系统分类:论文交流

  昨天是个值得庆贺的日子,因为连续两年中了IEEE TSP,虽然不是信号处理科班出身,但是经过20余年的努力,终于能够在SP方面刊出论文了,虽然小众化,虽然没有人引用,虽然也可能认为是垃圾论文,但是终究在自己探索的道路上,对于未知的认识前进了一大步,也行作为一种“垃圾”知识的载体,起到些微的作用,那就告慰终身了,也向着自己出版书籍的道路上铺下坚实的一章,我要写书啦!


    16-Jul-2019

Prof. Fabing Duan
Qingdao University
Qingdao
China
266071

Paper: T-SP-24342-2018.R2 "Noise Benefits in Combined Nonlinear Bayesian Estimators".

Dear Prof. Fabing Duan:

We are pleased to inform you that the above manuscript has been ACCEPTED for publication as a REGULAR paper in the IEEE Transactions on Signal Processing.


Publication Title: Transactions on Signal Processing
Article Title: Noise Benefits in Combined Nonlinear Bayesian Estimators
Author(s): Duan, Fabing; Pan, Yan; Chapeau-Blondeau, Francois; Abbott, Derek
Author E-mail: fabing.duan@hotmail.com
eCF Paper Id: T-SP-24342-2018.R2
 
\begin{abstract}
This paper investigates the benefits of intentionally adding noise to a Bayesian estimator, which comprises a linear combination of a number of individual Bayesian estimators that are perturbed by mutually independent noise sources and multiplied by a set of adjustable weighting coefficients. We prove that the Bayes risk for the mean square error (MSE) criterion is minimized when the same optimum weighting coefficients are assigned to the identical estimators in the combiner. This property leads to a simplified analysis of the noise benefit to the MSE of the combined Bayesian estimator even when the number of individual estimators tends to infinity. It is shown that, for a sufficiently large number of individual estimators, the MSE of the designed Bayesian estimator approaches a plateau for a wide range of added noise levels. This robust feature facilitates the incorporation of the added noise into the design of Bayesian estimators without tuning the noise level. For an easily implementable Bayesian estimator composed of quantizers, the benefit of the symmetric scale-family noise is demonstrated, and the optimal noise probability density function is approximated by solving a constrained nonlinear optimization problem. We further extend this potential Bayesian estimator to the nonlinear filter design. Finally, examples of the noise benefits in random parameter estimation and nonlinear filtering support the theoretical analyses.
\end{abstract} ....


战斗吧,达瓦里希!



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