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

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

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- 文章访问数: 1620
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- 被引次数: 0
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

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

摘要

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。
- 信号处理 /
- 水面目标识别 /
- 波形结构特征 /
- 支持向量机 /
- 优化算法
Abstract: According to research findings of speech acoustics, the timbre is applied to identify different types of targets. Since the information of timbre is indicated in the wave structure of time series, the feature of wave structure can be?extracted to classify various marine acoustic targets. The method of feature extraction based on wave structure is studied. The nine-dimension feature vector is constructed on the basis of signal statistical characteristics, including zero-crossing wavelength, peek-to-peek amplitude, zero-crossing-wavelength difference, wave train areas and so on. And the Support Vector Machine (SVM) is applied as a classifier for two kinds of marine acoustic target signals. The kernel function is set Radial Basis Function (RBF). The penalty?factor and parameter of RBF are properly selected by the method of combination of Differential Evolution (DE) and Particle Swarm Optimization (PSO), which helps to obtain better recognition rates than the grid search method.
- Signal processing /
- Marine target recognition /
- Wave structure /
- Support Vector Machine (SVM) /
- Optimization algorithm

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