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文章摘要
基于分段CEEMD降噪的时延估计研究
Research on time delay estimation based on signal chopping-based CEEMD de-noising method
Received:June 03, 2015  Revised:October 23, 2015
DOI:
中文关键词: CEEMD  信号分段  广义互相关  时延估计  降噪
英文关键词: CEEMD, signal chopping, generalized cross-correlation, time delay estimation, de-noising
基金项目:电子测试技术国防科技重点实验室项目(9140C120404140C12062)
Author NameAffiliationE-mail
Liu Ying* nuc lovelyangel1212@163.com 
Guo Yali nuc 861453364@163.com 
Han Yan nuc 1132547337@qq.com 
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中文摘要:
      为了提高信号非平稳、采样点数较多条件下的时延估计精度和速度,提出了一种基于分段(Complementary Ensemble Empirical Mode Decomposition,CEEMD) CEEMD降噪的广义互相关时延估计方法。该方法首先将原始信号划分为等长的小信号段,再利用CEEMD降噪法滤除各小段信号的噪声成分,连接消噪小信号段构成原始信号的降噪信号,最后根据降噪信号的互相关峰值来估计时延。针对该方法中的分段段数和添噪次数的选取问题,在研究这两个参数对仿真人走动信号去噪精度和计算耗时等性能的影响的基础上,给出了参数的具体选择范围。将该方法应用于声定位系统的时延估计中,结果表明:在信号采样点数较多、低信噪比的情况下该方法仍能够较为快速、准确地估计时延。
英文摘要:
      In order to improve the accuracy and speed of time-delay estimation(TDE) in the case that the signals are non-stationary and contain a large number of sampling points, a new method for generalized cross-correlation TDE based on signal chopping-based CEEMD (Complementary Ensemble Empirical Mode Decomposition, CEEMD) de-noising method is proposed. In this method, firstly the original large-signal is devided into several smaller sections of equal, then each section is de-noised using CEEMD de-noising method, all the smaller sections are combined to obtain the de-noised version of the original signal, finally the time delay value is estimated according to the cross-correlation peak of the de-noised signals. For the selection of the section number and the number of added noise in the process of TDE, the specific range of these two parameters is given by exploring the effect of these two parameters on de-noising precision, calculation time and other performance of the simulated people walking signal. Finally, this method is applied to the TDE in acoustic source localization system, results show that, this method can still rapidly and accurately estimate time delay value when the signals contain a large number of sampling points and the SNR is low.
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