基于小波包和BP神经网络的周界入侵防御系统目标识别

周求湛; 王聪香; 李亚强

doi:10.3788/OPE.20152313.0807

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基于小波包和BP神经网络的周界入侵防御系统目标识别

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- 基于小波包和BP神经网络的周界入侵防御系统目标识别
- Target recognition of perimeter intrusion defense system based on wavelet packet and BP neural network
- 光学精密工程 2015年23卷第10z期页码：806-813
- 作者机构：
  
  吉林大学通信工程学院, 吉林长春 130022
- 作者简介：
- 基金信息：
  
  装后面上项目**003()
- DOI：10.3788/OPE.20152313.0807
  中图分类号： P315.62;TP391.4
- 收稿日期：2015-06-19，
  
  修回日期：2015-07-07，
  
  纸质出版日期：2015-11-14
- 稿件说明：
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周求湛, 王聪香, 李亚强. 基于小波包和BP神经网络的周界入侵防御系统目标识别[J]. 光学精密工程, 2015,23(10z): 806-813

ZHOU Qiu-zhan, WANG Cong-xiang, LI Ya-qiang. Target recognition of perimeter intrusion defense system based on wavelet packet and BP neural network[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 806-813
周求湛, 王聪香, 李亚强. 基于小波包和BP神经网络的周界入侵防御系统目标识别[J]. 光学精密工程, 2015,23(10z): 806-813 DOI： 10.3788/OPE.20152313.0807.

ZHOU Qiu-zhan, WANG Cong-xiang, LI Ya-qiang. Target recognition of perimeter intrusion defense system based on wavelet packet and BP neural network[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 806-813 DOI： 10.3788/OPE.20152313.0807.

摘要

在周界入侵防御系统中地面目标产生的地震动信号较为微弱

因此在进行目标识别时需对目标信号进行特征提取。本文研究了基于小波包和BP神经网络的周界入侵防御系统目标识别方法。首先

使用小波去噪对前端探测器采集到的目标运动信号进行信号预处理

通过小波包分析将信号进行分解重构

对重构后的信号进行特征提取获得目标信号的特征向量。然后

将目标特征向量作为BP神经网络的输入

对各种类型的目标特征进行学习训练。最后

应用训练完成的网络对目标进行在线识别。提取地震检波器在6种距离下各5组共30组数据进行目标识别实验验证

结果显示

网络的实际输出向量和网络的期望输出向量是一致的

目标识别准确率达到99%左右。结果表明本方法可以有效识别周界入侵防御系统的各种目标。

Abstract

The seismic signals generated by recognizing targets in a perimeter intrusion defense system based on a seismometer are very weak and difficult to be directly identified. So the signal features of the targets need to be extracted before target identification.This paper presents a new method of target recognition based on wavelet packet analysis and BP neural networks.Firstly

target motion signals captured by a front detector were proposed by using wavelet denoising. Then

the signals were decomposed and reconstructed with wavelet packet analysis

and the feature values of reconstructed signals were extracted to construct feature vectors. Furthermore

the feature vectors were used as the inputs of the BP neural networks to carry on learning and training various types of target characteristics.Finally

the trained neural network were used to identify the targets on-line. Recognition result for 30 groups of data from the seismometer(6 kinds of distance

5 sets) shows that the desired output vector of the network and the actual output vector of the network is consistent

and the target recognition accuracy reaches to 99%. It concludes that this method can effectively identify the target of perimeter intrusion defense systems.

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Keywords

references

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