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J4  2012, Vol. 46 Issue (11): 2081-2088    DOI: 10.3785/j.issn.1008-973X.2012.11.021
电信技术     
基于二维特征矩阵的特征融合算法
鲍必赛1, 伍健荣1, 楼晓俊1, 刘海涛1,2
1. 中国科学院 上海微系统与信息技术研究所 无线传感器网络与通信重点实验室, 上海 200050;
2. 无锡物联网产业研究院, 无锡 214135
Feature fusion algorithm based on two-dimensional
feature matrix
BAO Bi-sai1, WU Jian-rong 1, LOU Xiao-jun1, LIU Hai-tao1, 2
1. Key Lab of Wireless Sensor Network and Communication, Shanghai Institute of Microsystem and Information
Technology, Chinese Academy of Science, Shanghai 200050, China;
2. Wuxi SensingNet Industrialization Research Institute, Wuxi 214135, China
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摘要:

为了提高无线传感器网络信息融合的效率, 提出一种多传感器二维特征融合(2DFF)策略. 将多个传感器标准化后的特征集组合成二维特征矩阵, 引入图像压缩技术, 包括二维主成分分析(2DPCA)及MatPCA对特征矩阵进行特征提取, 实现特征融合. 从理论上剖析该方法之所以能够有效地适用于特征融合, 且区别于传统方法的内在本质. 相比传统的特征融合方法, 该方法能够获得更加精确的融合特征, 提高信息融合的效率. 基于实地采集的地面目标信号的实验结果表明,该方法既提高目标识别率, 又降低了计算复杂度.

Abstract:

A strategy of two-dimensional feature fusion (2DFF) was proposed to improve efficiency of information fusion in wireless sensor networks (WSNs). The feature sets after normalized of multi-sensors were combined into a two-dimensional feature matrix. The techniques of image compression, twodimensional principal component analysis (2DPCA) and MatPCA were generalized for feature extraction from two-dimensional feature matrix to achieve multi-sensors feature fusion. The inherent essence of this method used in feature fusion was analyzed further in theory. Compared to traditional feature fusion method, this method can obtain more accurate fused feature and improve efficiency of information fusion. The experiment results on the real signals of ground targets show that this method can increase classification accuracy and reduce computational complexity.

出版日期: 2012-12-11
:  TP 391  
基金资助:

 国家“十一五”重大科技专项资助项目(2011ZX03005-006); 国家“973”重点基础研究发展规划资助项目(2011CB302906).

通讯作者: 刘海涛, 男, 研究员.     E-mail: liuhaitao@wsn.cn
作者简介: 鲍必赛(1984-), 男, 博士生, 从事信号与信息处理、目标识别及信息融合的研究. E-mail: baobisai@gmail.com
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引用本文:

鲍必赛, 伍健荣, 楼晓俊, 刘海涛. 基于二维特征矩阵的特征融合算法[J]. J4, 2012, 46(11): 2081-2088.

BAO Bi-sai, WU Jian-rong , LOU Xiao-jun, LIU Hai-tao. Feature fusion algorithm based on two-dimensional
feature matrix. J4, 2012, 46(11): 2081-2088.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2012.11.021        http://www.zjujournals.com/eng/CN/Y2012/V46/I11/2081

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