| 无线传感网络中的目标分类融合 |
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| 引用本文： | 刘春婷, 霍宏, 方涛, 李德仁, 沈晓. 无线传感网络中的目标分类融合. 自动化学报, 2006, 32(6): 947-955. |
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| 作者姓名： | 刘春婷 霍宏 方涛 李德仁 沈晓 |
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| 作者单位： | 1.Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240;;2.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079 |
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| 摘 要： | In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor andDempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.
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| 关 键 词： | Wireless sensor networks classification fusion wavelet decomposition weighted k-nearest-neighbor Dempster-Shafer theory |
| 收稿时间： | 2005-12-08 |
| 修稿时间： | 2006-03-21 |
Classification Fusion in Wireless Sensor Networks |
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| LIU Chun-Ting, HUO Hong, FANG Tao, LI De-Ren, SHEN Xiao. Classification Fusion in Wireless Sensor Networks. ACTA AUTOMATICA SINICA, 2006, 32(6): 947-955. |
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| Authors: | LIU Chun-Ting HUO Hong FANG Tao LI De-Ren SHEN Xiao |
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| Affiliation: | 1. Institute of Image Processing &Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079 |
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| Abstract: | In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor and Dempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved. |
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| Keywords: | Wireless sensor networks classification fusion wavelet decomposition weighted knearest-neighbor Dempster-Shafer theory |
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