Noise enhanced parameter estimation

Hao Chen; Pramod K. Varshney; James H. Michels

doi:10.1109/TSP.2008.928508

Noise enhanced parameter estimation

Hao Chen, Pramod K. Varshney, James H. Michels

Department of Electrical Engineering & Computer Science

Research output: Contribution to journal › Article › peer-review

56 Scopus citations

Abstract

This paper investigates the phenomenon of noise enhanced systems for a general parameter estimation problem. When the estimator is fixed and known, the estimation performance before and after the addition of noise are evaluated. Performance comparisons are made between the original estimators and noise enhanced estimators based on different criteria. The form of the optimal noise probability density function (pdf) is determined. The results are further extended to the general case where the noise is introduced to the system via a transformation. For the case where the estimator is fixed and unknown, approaches are also proposed to find the optimum noise. Finally, two illustrative examples are presented where the performance comparison is made between the optimal noise modified estimator and Gaussian noise modified estimator.

Original language	English (US)
Pages (from-to)	5074-5081
Number of pages	8
Journal	IEEE Transactions on Signal Processing
Volume	56
Issue number	10 II
DOIs	https://doi.org/10.1109/TSP.2008.928508
State	Published - 2008

Keywords

Bayesian estimation
Noise enhanced estimation
Parameter estimation
Stochastic Resonance

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering

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10.1109/TSP.2008.928508

Cite this

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title = "Noise enhanced parameter estimation",

abstract = "This paper investigates the phenomenon of noise enhanced systems for a general parameter estimation problem. When the estimator is fixed and known, the estimation performance before and after the addition of noise are evaluated. Performance comparisons are made between the original estimators and noise enhanced estimators based on different criteria. The form of the optimal noise probability density function (pdf) is determined. The results are further extended to the general case where the noise is introduced to the system via a transformation. For the case where the estimator is fixed and unknown, approaches are also proposed to find the optimum noise. Finally, two illustrative examples are presented where the performance comparison is made between the optimal noise modified estimator and Gaussian noise modified estimator.",

keywords = "Bayesian estimation, Noise enhanced estimation, Parameter estimation, Stochastic Resonance",

author = "Hao Chen and Varshney, {Pramod K.} and Michels, {James H.}",

note = "Funding Information: Manuscript received March 29, 2007; revised September 6, 2007. First published July 15, 2008; current version published September 17, 2008. The associate editor coordinating the review of this paper and approving it for publication was Dr. Thierry Blu. This work was supported by AFOSR under Contract FA9550-06-C-0036.",

year = "2008",

doi = "10.1109/TSP.2008.928508",

language = "English (US)",

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pages = "5074--5081",

journal = "IEEE Transactions on Signal Processing",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "10 II",

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TY - JOUR

T1 - Noise enhanced parameter estimation

AU - Chen, Hao

AU - Varshney, Pramod K.

AU - Michels, James H.

N1 - Funding Information: Manuscript received March 29, 2007; revised September 6, 2007. First published July 15, 2008; current version published September 17, 2008. The associate editor coordinating the review of this paper and approving it for publication was Dr. Thierry Blu. This work was supported by AFOSR under Contract FA9550-06-C-0036.

PY - 2008

Y1 - 2008

N2 - This paper investigates the phenomenon of noise enhanced systems for a general parameter estimation problem. When the estimator is fixed and known, the estimation performance before and after the addition of noise are evaluated. Performance comparisons are made between the original estimators and noise enhanced estimators based on different criteria. The form of the optimal noise probability density function (pdf) is determined. The results are further extended to the general case where the noise is introduced to the system via a transformation. For the case where the estimator is fixed and unknown, approaches are also proposed to find the optimum noise. Finally, two illustrative examples are presented where the performance comparison is made between the optimal noise modified estimator and Gaussian noise modified estimator.

AB - This paper investigates the phenomenon of noise enhanced systems for a general parameter estimation problem. When the estimator is fixed and known, the estimation performance before and after the addition of noise are evaluated. Performance comparisons are made between the original estimators and noise enhanced estimators based on different criteria. The form of the optimal noise probability density function (pdf) is determined. The results are further extended to the general case where the noise is introduced to the system via a transformation. For the case where the estimator is fixed and unknown, approaches are also proposed to find the optimum noise. Finally, two illustrative examples are presented where the performance comparison is made between the optimal noise modified estimator and Gaussian noise modified estimator.

KW - Bayesian estimation

KW - Noise enhanced estimation

KW - Parameter estimation

KW - Stochastic Resonance

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JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

IS - 10 II

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Noise enhanced parameter estimation

Abstract

Keywords

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