| 水下目标特征提取方法研究 |
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| 引用本文： | 郭丽华,王大成,丁士圻.水下目标特征提取方法研究[J].声学技术,2005,24(3):148-151,156. |
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| 作者姓名： | 郭丽华 王大成 丁士圻 |
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| 作者单位： | 哈尔滨工程大学水声工程学院,哈尔滨,150001 |
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| 摘 要： | 有效的特征提取技术是水雷目标识别的基础。文章采用了两种前期研究中较为有效的水雷目标特征提取方法(频域离散小波变换法和常数Q滤波子带能量法),并引入了一种应用在水下目标识别领域中的特征提取方法(波形结构法)。应用此三种特征提取方法提取的特征来识别实雷目标以及假目标,分类器采用三层BP算法的前向神经网络,给出了具体的识别率,说明该特征提取算法是有效的,用波形结构法进行水雷目标的特征提取是可行的。
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| 关 键 词： | 水雷目标识别 特征提取 波形结构 人工神经网络 |
| 文章编号： | 1000-3630（2005）03-0148-04 |
| 收稿时间： | 2004-05-25 |
| 修稿时间： | 2004-05-252004-07-11 |
Extraction of features of underwater target |
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| GUO Li-hu,WANG Da-cheng and DING Shi-qi.Extraction of features of underwater target[J].Technical Acoustics,2005,24(3):148-151,156. |
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| Authors: | GUO Li-hu WANG Da-cheng and DING Shi-qi |
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| Affiliation: | College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China;College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China;College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China |
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| Abstract: | Effective feature extraction is fundamental in the underwater mine recognition. Two feature extraction techniques effective in the mine recognition are discussed, namely, frequency discrete wave transform (FDWT) and Q,filter. A feature extraction technique, wave structure (WS), is introduced for underwater target recognition. The achieved recognition ratio with 3,layer BP network indicates the effectiveness of the applied techniques and feasibility of the WS technique used in feature extraction of mines. |
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| Keywords: | mine target recognition feature extraction waveform structure artificial neural network |
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