CAJ | 학술논문

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。
차감어음성학적연구성과，음색가작위구분불동목표적의거。유우함선복사조성적음색신식포함재기신호적파형결구특정중，가이통과제취함선복사조성적파형결구특정판단목표류형。해문대수면목표신호시역파형결구특정제취진행료연구，구건료기우신호통계특성적특정시량，포괄과영점파장、봉봉폭도、과영점파장차분이급파렬면적등。응용지지향량궤(Support Vector Machine, SVM)작위분류기식별량류수면목표신호，핵함수위경향기함수(RBF)。제출료차분진화화입자군산법적혼합산법，우화료징벌인자화경향기함수삼수적선취，량류목표적식별솔교상규적망격수색법유현저제고。
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.