小波阈值去噪在传感器性能试验数据处理中的应用  

Application of wavelet threshold de-noising in sensor performance test data processing

作　　者：田丰[1] 孙剑[1] 邵山[2] 

机构地区：[1]沈阳航空航天大学自动化学院,辽宁沈阳110136 [2]沈阳飞机设计研究所,辽宁沈阳110136

出　　处：《传感器与微系统》2014年第6期143-146,共4页Transducer and Microsystem Technologies

摘　　要：在传感器性能测试中,采样的信号经常受到各种噪声的干扰和污染,不能准确反映设备的运行状态,不宜直接用于数据处理与分析。为了对试验数据进行去噪预处理,根据具体传感器性能试验数据的特点,运用小波变换方法,确定了适合的小波去噪参数。引入重构因子比较几种常用的小波基,应用Matlab对仿真信号进行小波阈值去噪处理,依据平滑度确定了分解层数,为传感器性能试验数据预处理提供了的一种有效的小波阈值去噪方法。In sensor performance test, signal of sampling is often subjected to interference and pollution of all kinds of noise , which can not accurately reflect operating status of equipment, and should not be directly used for data processing and analysis. In order to carry out de-noise preprocessing on test datas, according to characteristics of specific sensor test data, using wavelet transform method,preprocessing wavelet determine approperate wavelet denoising parameters. Introduce reconstruction factor,compare several kinds of commonly used wavelet basis,and then decomposition level is determined according to smoothness, simulation signal is processed by wavelet threshold de-noising use Matlab, which provide an effective wavelet threshold de-noising method for character test data preprocessing of sensor.

关 键 词：小波去噪 阈值 重构因子 平滑度 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置] V557[自动化与计算机技术—控制科学与工程]