摘要
针对强背景噪声下信噪比极低的微弱特征信号的识别问题,提出了基于奇异值分解的随机共振特征提取方法.该方法首先利用奇异值分解对实际采样信号进行预处理和重构,然后寻找到特征信号分量与噪声强度相匹配的分量信号.此分量信号再经过非线性双稳系统的随机共振处理,可实现从强噪声背景中检测极微弱的特征信号.
In order to detect the weak characteristic signal submerged in heavy noise with extremely low signal-to-noise ratio, a method based on singular value decomposition (SVD) and stochastic resonance is proposed. The sampling signal is first preprocessed and reconstructed by means of SVD, and then we search for a component signal. In the component signal, the components of the characteristic signal match noise strength. Then the component signal is processed with the non-linear bistable system to obtain stochastic resonance response, thus the goal of detecting the weak characteristic signal submerged in a heavy background noise is realized.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2012年第21期77-85,共9页
Acta Physica Sinica
基金
国家自然科学基金(批准号:50975202)
北京市先进制造技术重点实验室开放项目(批准号:001000546612018)资助的课题~~
关键词
奇异值分解
随机共振
噪声
singular value decomposition
stochastic resonance
noise