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基于波形结构特征和支持向量机的水面目标识别

孟庆昕 杨士莪 于盛齐

孟庆昕, 杨士莪, 于盛齐. 基于波形结构特征和支持向量机的水面目标识别[J]. 电子与信息学报, 2015, 37(9): 2117-2123. doi: 10.11999/JEIT150139
引用本文: 孟庆昕, 杨士莪, 于盛齐. 基于波形结构特征和支持向量机的水面目标识别[J]. 电子与信息学报, 2015, 37(9): 2117-2123. doi: 10.11999/JEIT150139
Meng Qing-xin, Yang Shi-e, Yu Sheng-qi. Recognition of Marine Acoustic Target Signals Based on Wave Structure and Support Vector Machine[J]. Journal of Electronics and Information Technology, 2015, 37(9): 2117-2123. doi: 10.11999/JEIT150139
Citation: Meng Qing-xin, Yang Shi-e, Yu Sheng-qi. Recognition of Marine Acoustic Target Signals Based on Wave Structure and Support Vector Machine[J]. Journal of Electronics and Information Technology, 2015, 37(9): 2117-2123. doi: 10.11999/JEIT150139

基于波形结构特征和支持向量机的水面目标识别

doi: 10.11999/JEIT150139
基金项目: 

国家自然科学基金(11234002)资助课题

Recognition of Marine Acoustic Target Signals Based on Wave Structure and Support Vector Machine

  • 摘要: 借鉴语音声学的研究成果,音色可作为区分不同目标的依据。由于舰船辐射噪声的音色信息包含在其信号的波形结构特征中,可以通过提取舰船辐射噪声的波形结构特征判断目标类型。该文对水面目标信号时域波形结构特征提取进行了研究,构建了基于信号统计特性的特征矢量,包括过零点波长、峰峰幅度、过零点波长差分以及波列面积等。应用支持向量机(Support Vector Machine, SVM)作为分类器识别两类水面目标信号,核函数为径向基函数(RBF)。提出了差分进化和粒子群算法的混合算法,优化了惩罚因子和径向基函数参数的选取,两类目标的识别率较常规的网格搜索法有显著提高。
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出版历程
  • 收稿日期:  2015-01-27
  • 修回日期:  2015-04-20
  • 刊出日期:  2015-09-19

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