EEMD-based vehicle micro-motion echo signal extraction and classification

LIN Ping; CHEN Hua-jie; LIN Feng-xiao

doi:10.13873/J.1000-9787(2017)10-0038-03

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EEMD-based vehicle micro-motion echo signal extraction and classification

更新时间：2025-09-11

- EEMD-based vehicle micro-motion echo signal extraction and classification
- Vol. 36, Issue 10, Pages: 38-40+44(2017)
- 作者机构：
  
  杭州电子科技大学通信信息传输与融合技术国防重点学科实验室
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2017)10-0038-03
  CLC： TN957.51
- Published：2017
- 稿件说明：
移动端阅览
[1]林萍,陈华杰,林封笑.基于EEMD的车辆微动信号提取及分类[J].传感器与微系统,2017,36(10):38-40+44.

LIN Ping, CHEN Hua-jie, LIN Feng-xiao. EEMD-based vehicle micro-motion echo signal extraction and classification[J]. 2017, 36(10): 38-40+44.
[1]林萍,陈华杰,林封笑.基于EEMD的车辆微动信号提取及分类[J].传感器与微系统,2017,36(10):38-40+44. DOI： 10.13873/J.1000-9787(2017)10-0038-03.

LIN Ping, CHEN Hua-jie, LIN Feng-xiao. EEMD-based vehicle micro-motion echo signal extraction and classification[J]. 2017, 36(10): 38-40+44. DOI： 10.13873/J.1000-9787(2017)10-0038-03.

摘要

针对轮式和履带式车辆微动信号的差异对目标车辆进行了识别分类

利用集合经验模式分解（EEMD）将原始信号分解为多个本征模函数（IMF）

通过相关性分析

验证了EEMD能够有效克服EMD所带来的模态混叠问题。在此基础上

提取了4种特征

采用最近邻方法进行分类。实验结果表明:经EEMD所提取的特征是有效的

对目标速度

以及方位角的变化具有相当的稳健性。

Abstract

Aiming at difference of micro-motion echo signal of two kinds of vehicles

target vehicle is identified and classified. Ensemble empirical mode decomposition（ EEMD） is employed to decompose original signal into a number of intrinsic mode function（ IMF）. By means of correlation analysis

it is proved that EEMD can effectively overcome the mode mixing problem caused by EMD. On this basis

four features are extracted

the nearest neighbor method is used for classification. Experimental results show that the features extracted after EEMD are effective and fairly robust against the variation of the target velocity and azimuth angle.

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references

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