Optimal Noise-Boosted Estimator Design Via Adaptive Stochastic Resonance

被引:1
|
作者
Pan, Yan [1 ]
Xu, Liyan [2 ]
Duan, Fabing [3 ]
Chapeau-Blondeau, Francois [4 ]
机构
[1] Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266590, Peoples R China
[2] Univ Qingdao, Sch Elect Informat, Qingdao 266071, Peoples R China
[3] Univ Qingdao, Inst Complex Sci, Qingdao 266071, Peoples R China
[4] Univ Angers, LARIS, 62 Ave Notre Dame du Lac, Angers F-49000, France
来源
FLUCTUATION AND NOISE LETTERS | 2023年 / 22卷 / 03期
基金
中国国家自然科学基金;
关键词
Adaptive stochastic resonance; Signal estimation; Mean squared error; Optimization space; Learning capacity; SYSTEMS;
D O I
10.1142/S0219477523500281
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In signal estimation, an optimal estimator is frequently unachievable because its closed form may not be analytically tractable or is too complex to implement. Alternatively, one can turn to suboptimal yet easily implementable estimators for practical signal estimation tasks. In this paper, an optimal noise-boosted estimator is designed and the adaptive stochastic resonance method is implemented to simultaneously exploit the beneficial role of the injected noise as well as the learning ability of the estimator parameter. Aiming to effectively improve the estimation performance, we use the kernel function method to find an approximate solution for the probability density function (PDF) of the optimal injected noise. During this process, the noise PDF and the estimator parameter establish a finite-dimensional non-convex optimization space for maximizing the estimation performance, which is adaptively searched by the sequential quadratic programming (SQP) algorithm at each iteration. Two representative estimation problems are explored. The obtained results demonstrate that this adaptive stochastic resonance method can improve the performance of the suboptimal estimators and bring it very close to that of the optimal estimator.
引用
收藏
页数:17
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