摘要
为了在传感器测量锂电池非连续性膜厚前不需测量C型机构的固有频率和扫描振动频率,采用3层小波-阈值判断-稀疏分解信号处理去噪方法,进行了理论分析和实验验证。该方法不需固有频率和扫描振动频率的先验知识,在不同C型机构扫描速率模式下,通过迭代选取最佳匹配的原子序列保留锂电池薄膜厚度分布,滤除局部噪声波动,实现稀疏迭代去噪。结果表明,相对于小波算法,在缺乏先验知识的条件下,稀疏分解算法具有较好的去噪性能,其均方差值达5μm^7μm,是一种操作简单、可行有效的方法。
In order to avoid measuring the inherent frequency and the scanning vibration frequency of C-dynamic scanning system before measuring discontinuous film thickness of lithium battery with laser sensors,the 3-layer waveletthreshold judgment-sparse decomposition signal processing de-noising method was used.Theoretical analysis and experimental verification were made.Without prior knowledge of the inherent frequency and the scanning vibration frequency and under different C-dynamic scanning mode,the best-matching atomic sequence was selected by iteration and the film thickness distribution of lithium battery was reserved,fluctuations of the local noise were filtered and sparse iterative de-noising was realized.The results show that comparing with the wavelet algorithm and in the absence of the prior knowledge,sparse decomposition algorithm has better de-noising performance and is a simple,practical and effective method.Mean square error of sparse decomposition algorithm is 5μm ～ 7μm.
出处
《激光技术》
CAS
CSCD
北大核心
2014年第4期546-550,共5页
Laser Technology
基金
江苏省自然科学基金资助项目(BK20130245)
江苏省常州市科技计划资助项目(CE20120071)
江苏省常州市高新区科技发展计划资助项目(XE120121408)
常州市光电子材料与器件重点实验室资助项目(20130694)
关键词
信号处理
去噪
稀疏分解
锂电池薄膜
signal processing
de-noising
sparse decomposition
film of lithium battery
