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

Meng Qing-xin; Yang Shi-e; Yu Sheng-qi

doi:10.11999/JEIT150139

Volume 37 Issue 9

Sep. 2015

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(9): 2117-2123

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 & 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 & Information Technology, 2015, 37(9): 2117-2123. doi: 10.11999/JEIT150139

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

doi: 10.11999/JEIT150139

Received Date: 2015-01-27
Rev Recd Date: 2015-04-20
Publish Date: 2015-09-19

Abstract

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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References

王志伟. 水下目标被动识别技术方法研究[D]. [硕士论文], 哈尔滨工程大学, 2002.

Wang Zhi-wei. The recognition study of passivity target[D]. [Master dissertation], Harbin Engineering University, 2002.

蔡悦斌, 张明之, 史习智, 等. 舰船噪声波形结构特征提取及分类研究[J]. 电子学报, 1999, 27(6): 129-130.

Cai Yue-bin, Zhang Ming-zhi, Shi Xi-zhi, et al.. The feature extraction and classification of ocean acoustic signals based on wave structure[J]. Acta Electronica Sinica, 1999, 27(6): 129-130.

廖明熙, 张小蓟, 张歆. 基于稀疏表示的水声信号分类识别[J]. 探测与控制学报, 2014, 36(4): 67-70.

Liao Ming-xi, Zhang Xiao-ji, and Zhang Xin. Classification and recognition of underwater acoustic signal based on sparse representation[J].Journal of Detection Control, 2014, 36(4): 67-70.

许文海, 续元君, 董丽丽, 等. 基于水平集和支持向量机的图像声呐目标识别[J]. 仪器仪表学报, 2012, 33(1): 49-55.

Xu Wen-hai, Xu Yuan-jun, Dong Li-li, et al.. Level-set and SVM based target recognition of image sonar[J].Chinese Journal of Scientific Instrument, 2012, 33(1): 49-55.

史亚, 姬红兵, 朱明哲, 等. 多核融合框架下的雷达辐射源个体识别[J]. 电子与信息学报, 2014, 36(10): 2484-2490.

Shi Ya, Ji Hong-bing, Zhu Ming-zhe, et al.. Specific radar emitter identification in multiple kernel fusion framework[J]. Journal of Electronics Information Technology, 2014, 36(10): 2484-2490.

丁凯, 方向, 张卫平, 等. 基于声信号多重分形和支持向量机的目标识别研究[J]. 兵工学报, 2012, 33(12): 1521-1526.

Ding Kai, Fang Xiang, Zhang Wei-ping, et al.. Target identification of acoustic signals based on multi-fractal analysis and support vector machine[J]. Acta Armamentarii, 2012, 33(12): 1521-1526.

张启忠, 席旭刚, 罗志增. 基于非线性特征的表面肌电信号模式识别方法[J]. 电子与信息学报, 2013, 35(9): 2054-2058.

Zhang Qi-zhong, Xi Xu-gang, and Luo Zhi-zeng. A pattern recognition method for surface electromyography based on nonlinear features[J]. Journal of Electronics Information Technology, 2013, 35(9): 2054-2058.

Chen Ping-wei, Wang Yung-ying, and Lee Ming-hahn. Model selection of SVMs using GA approach[C]. Proceedings of 2004 IEEE International Joint Conference on Neural Networks, Pscataway, NJ, 2004: 2035-2040.

周绍磊, 廖剑, 史贤俊. RBF-SVM的核参数选择方法及其在故障诊断中的应用[J]. 电子测量与仪器学报, 2014, 28(3): 240-246.

Zhou Shao-lei, Liao Jian, and Shi Xian-jun. Kernel parameter selection of ＲBM-SVM and its application in fault diagnosis [J]. Journal of Electronic Measurement and Instrumentation, 2014, 28(3): 240-246.

郑适, 张安学, 岳思橙, 等. 基于改进粒子群优化的探地雷达波形反演算法[J]. 电子与信息学报, 2014, 36(11): 2717-2722.

Zheng Shi, Zhang An-xue, Yue Si-cheng, et al.. Ground penetrating radar inversion algorithm based on improved particle swarm optimization[J]. Journal of Electronics Information Technology, 2014, 36(11): 2717-2722.

Fernandez P J A, Baeten V, Renier A M, et al.. Combination of support vector machines (SVM) and near-infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds[J]. Journal of Chemometrics, 2004(18): 341-349.

Cristianini N and Taylor J S. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods[M]. Cambridge: The Press Syndicate of the University of Cambridge, 2001: 93-94.

廖晓晰. 动力系统的稳定性理论和应用[M]. 北京：国防工业出版社, 2000: 169-199.

Liao Xiao-xi. Stability Theory and Application of Power System[M]. Beijing: National Defence Industry Press, 2000: 169-199.

杨坤德. 水声阵列信号的匹配场处理[M]. 西安: 西北工业大学出版社, 2008: 32-39.

Yang Kun-de. Matched Field Processing of Underwater Acoustic Array Signals[M]. Xian: Press of Northwestern Polytechnical University, 2008: 32-39.

纪震，廖惠连，吴青华. 粒子群算法及应用[M]. 北京: 科学出版社，2009: 169-199.

Ji Zhen, Liao Hui-lian, and Wu Qing-hua. Application of Particle Swarm Algorithm[M]. Beijing: Science Press, 2009: 169-199.

Clerc M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization[C]. Proceedings of the Congress of Evolutionary Computation. Washington, USA, 1999: 1951-1957.

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