Stochastic resonance in Bayesian estimation and CRLB for nonlinear system

被引：0

作者：

Yang, Ting ^{[1
]}

Liu, Shujun ^{[2
]}

Liu, Hongqing ^{[3
]}

Zhang, Kui ^{[2
]}

Guo, Zhiwei ^{[1
]}

Yang, Shiju ^{[1
]}

Li, Yu ^{[1
]}

机构：

[1] Chongqing Technol & Business Univ, Chongqing Engn Lab Detect Control & Integrated Sys, Chongqing 400067, Peoples R China

[2] Chongqing Univ, Coll Microelect & Commun Engn, Chongqing 400044, Peoples R China

[3] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Mobile Commun Technol, Chongqing 400065, Peoples R China

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2023年 / 609卷

基金：

中国国家自然科学基金;

关键词：

Stochastic resonance; Bayesian estimation; Mean square error; Cramer-Rao lower bound; NOISE BENEFITS; PARAMETER-ESTIMATION; GAIN;

D O I：

10.1016/j.physa.2022.128338

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

In this work, noise enhanced parameter estimation problems are investigated for a general nonlinear system, where an additive noise is added to the nonlinear system input and a Bayesian estimator is developed based on the noise modified output. The optimal probability distribution of the additive noises is formulated successively for minimizing the mean square error (MSE) of the optimal Bayesian estimation and the Cramer-Rao lower bound (CRLB). Then the optimal additive noises for the two different noise enhanced optimization problems are explicitly derived as constant vectors, which implies the randomization of constant vectors is not beneficial to these optimizations. Finally, numerical results are presented to illustrate the theoretical results.(c) 2022 Elsevier B.V. All rights reserved.

引用

页数：15

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