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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 Microsystem and Information
Technology, Chinese Academy of Science, Shanghai 200050, China;
2. Wuxi SensingNet Industrialization Research Institute, Wuxi 214135, China |
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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, twodimensional 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．
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Published: 11 December 2012
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基于二维特征矩阵的特征融合算法
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.
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