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Type: Journal article
Title: Noise benefits in combined nonlinear Bayesian estimators
Author: Duang, F.
Pan, Y.
Chapeau-Blondeau, F.
Abbott, D.
Citation: IEEE Transactions on Signal Processing, 2019; 67(17):4611-4623
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2019
ISSN: 1053-587X
Statement of
Fabing Duan, Yan Pan, François Chapeau-Blondeau, and Derek Abbott
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.
Keywords: Noise benefit; Bayesian estimator; linear combination; nonlinear filtering; stochastic resonance
Rights: © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
RMID: 0030133732
DOI: 10.1109/TSP.2019.2931203
Appears in Collections:Electrical and Electronic Engineering publications

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