基于EEMD的多传感器信息融合降噪方法  被引量:4

Multi-sensor information fusion denoising method based on EEMD

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作  者:吕艳新[1] 李海涛[1] 邓冬[1] 张跃[1] 赵立彬[1] 卫东[1] 

机构地区:[1]环境保护部核与辐射安全中心,北京100082

出  处:《传感器与微系统》2014年第10期5-7,10,共4页Transducer and Microsystem Technologies

摘  要:由于传感器被动采集所得信号没有太多先验信息,在后续应用过程中因噪声的存在受到限制,所以,提出一种基于总体经验模式分解(EEMD)和时延估计的多传感器信息融合降噪方法。首先将多传感器采集所得信号进行EEMD,将所得对应IMF分量进行互相关,求取时延估计值,依据时延矢量封闭准则(TDVCR)获得相应IMF分量的时延矢量误差。最后,根据多传感器综合支持度确定相应权重,对IMF分量进行重构,得到降噪后的信号。实验结果表明:该方法在EEMD的基础上有效利用了多传感器的时延估计特性,在没有任何先验信息的条件下降低了信号噪声,取得满意的效果。Application of signals passively acquired by sensor are always restricted because of no much prior information, so an information fusion denoising method for multi-sensor based on ensemble empirical mode decomposition(EEMD) and time delay estimation is proposed, Firstly, signals acquired by multi-sensor are decomposed by EEMD, and obtained corresponding IMF components is cross correlated. Time delays are computed according to time delay vector close rule (TDVCR) , time delay vector error of corresponding IMF component is obtained. Finally, according to comprehensive support degree of muhi-sensor to determine eorrespording weight, IMF components are reconstructed to obtain denoising signals. The experimental results show that this method effectively uses characteristic of multi-sensor time delay estimation based on EEMD, and signal noises under no prior information is reduced, and obtain satisfied effect.

关 键 词:总体经验模式分解 时延矢量封闭准则 时延估计 多传感器 信息融合 降噪 

分 类 号:TP212.9[自动化与计算机技术—检测技术与自动化装置]

 

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