摘要
为快速恢复强噪声掩盖下的弱信号,提出一种基于ACEEMD(adaptive complete ensemble empirical mode decomposition)模型的弱信号快速重构方法。结合优化的互补集合经验模态分解与GPU并行运算,快速将弱信号从强噪声背景下重构出来,使其更容易被检测。运用该方法对模拟弱信号数据与实际弱信号数据进行处理,实验结果表明,处理后的测试数据信噪比提高了2db-3db,处理速度提高了4-5倍,处理效果明显。
To recover the weak signal with strong noise,a weak signal recovery method based on ACEEMD was proposed.The method combined the optimization of CEEMD and GPU parallel operation,and quickly recovered the weak signal from strong noise,making it easier be processed and identified.This method was used to process the simulated weak signal data and the real weak signal data.Experimental results show that the signal to noise ratio is increased by 2 dB to 3 dB,and the processing speed is increased by 4-5 times and the processing effect is obvious.
作者
文畅
廖虎
谢凯
贺建飚
WEN Chang;LIAO Hu;XIE Kai;HE Jian-biao(School of Computer Science,Yangtze University,Jingzhou 434023,China;School of Electronic Information,Yangtze University,Jingzhou 434023,China;School of Information Science and Engineering,Central South University,Changsha 410083,China)
出处
《计算机工程与设计》
北大核心
2018年第4期1111-1115,共5页
Computer Engineering and Design
基金
国家自然科学基金项目(61272147)
湖北省教育厅科学技术研究基金项目(B2015446)
关键词
弱信号
强噪声背景
互补集合经验模态分解
图形处理器
并行计算
weak signal
strong noise background
complete ensemble empirical mode decomposition
graphical processing unit
parallel computing
