Pyroelectric infrared signal recognition based on feature sub-pattern canonical correlation analysis

GONG Wei-guo; WANG Lin-hong; HE Li-fang

doi:10.3788/OPE.20111904.0884

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Pyroelectric infrared signal recognition based on feature sub-pattern canonical correlation analysis

Article | 更新时间：2020-08-12

- Pyroelectric infrared signal recognition based on feature sub-pattern canonical correlation analysis
- Optics and Precision Engineering Vol. 19, Issue 4, Pages: 884-891(2011)
- 作者机构：
  
  1. 重庆大学光电技术及系统教育部重点实验室重庆,400044
  2. 重庆电子工程职业学院电子信息系,重庆 401331
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20111904.0884
  CLC： TP3941.4;TN215
- Received：08 February 2010，
  
  Revised：16 July 2010，
  
  Published Online：26 April 2011，
  
  Published：26 April 2011
- 稿件说明：
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龚卫国, 王林泓, 贺莉芳. 基于特征子模式典型相关分析的热释电红外信号识别[J]. 光学精密工程, 2011,19(4): 884-891

GONG Wei-guo, WANG Lin-hong, HE Li-fang. Pyroelectric infrared signal recognition based on feature sub-pattern canonical correlation analysis[J]. Editorial Office of Optics and Precision Engineering, 2011,19(4): 884-891
龚卫国, 王林泓, 贺莉芳. 基于特征子模式典型相关分析的热释电红外信号识别[J]. 光学精密工程, 2011,19(4): 884-891 DOI： 10.3788/OPE.20111904.0884.

GONG Wei-guo, WANG Lin-hong, HE Li-fang. Pyroelectric infrared signal recognition based on feature sub-pattern canonical correlation analysis[J]. Editorial Office of Optics and Precision Engineering, 2011,19(4): 884-891 DOI： 10.3788/OPE.20111904.0884.

摘要

为使现有热释电红外(PIR)探测器具有识别检测区域内红外辐射源的功能

提出一种基于典型相关分析(CCA)特征融合的人体和非人体PIR信号识别方法。该方法首先提取PIR信号的频谱和小波包熵特征

然后对频谱进行子模式划分

并分别与小波包熵特征进行CCA融合

把融合后的结果作为判别信息

从而实现了特征融合且消除了特征之间的信息冗余。最后通过多数投票方式融合判别结果。作为子模式CCA特征融合的一种特殊情况

文中分析了特征与自身子模式特征CCA融合的分类性能。实验结果表明

当频谱分为5个子模式时

能有效地对人体和非人体红外辐射源进行识别

识别率可达95.2%

比直接采用频谱与小波包熵CCA融合的识别率提高了2.7%。而采用小波包熵与自身子模式特征CCA融合的识别率最高为90.7%

比单独采用小波包熵的识别率提高了2.3%。

Abstract

To improve the recognition ability of a pyroelectric infrared (PIR) detector for different infrared radiation sources

a method for human and non-human recognition based on Canonical Correlation Analysis (CCA) was proposed. Firstly

the frequency spectrum and wavelet packet entropy were extracted as features

and the spectrum was divided into sub-patterns. Then

each sub-pattern and wavelet packet entropy were fused with CCA method

and the fused feature was employed as classification information. By this way

the feature fusion was realized and the redundant information among the features was also eliminated. Finally

the recognition results were obtained by a majority voting method. As a special case of the sub-pattern fusion

the classification abilities of the features fused with their own sub-pattern were also studied in the paper. Experimental results show when the fre quency is divided into 5 sub-patterns

the recognition rate can reach 95.2%

which is higher 2.7% than that of only fusing the frequency and the wavelet packet entropy.Moreover

the recognition rate of wavelet packet entropy fused with its own sub-pattern is 90.7%

which is higher 2.3% than that of wavelet packet entropy.

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Keywords

references

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