基于EEMD的多传感器信息融合降噪方法

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.

Multi-sensor information fusion denoising method based on EEMD

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.