Citation: | CAO Tao, DENG Jianjing, YUE Ling, LI Yongsheng. Underwater Target Recognition Based on Dynamic Ensemble of Random Forest[J]. Journal of Unmanned Undersea Systems, 2024, 32(3): 552-557. doi: 10.11993/j.issn.2096-3920.2024-0054 |
[1] |
周德善, 李志舜, 朱邦元. 鱼雷自导技术[M]. 北京: 国防工业出版社, 2009.
|
[2] |
蒋兴舟. 鱼雷制导设计原理[M]. 武汉: 海军工程大学, 2001.
|
[3] |
何心怡, 高贺, 陈菁, 等. 鱼雷真假目标识别技术现状与展望[J]. 鱼雷技术, 2016, 24(1): 23-27.
He Xinyi, Gao He, Chen Jing, et al. Current situation and prospect on torpedo's true/false target identification technologies[J]. Torpedo Technology, 2016, 24(1): 23-27.
|
[4] |
王明洲, 郝重阳, 黄晓文. 基于相关法方位分析的水下主动目标尺度识别研究[J]. 西北工业大学学报, 2003, 21(3): 317-320.
Wang Mingzhou, Hao Chongyang, Huang Xiaowen. On underwater target dimension recognition based on bearings analysis of signal correlation feature[J]. Journal of Northwestern Polytechnical University, 2003, 21(3): 317-320.
|
[5] |
刘朝晖, 付战平, 王明洲. 基于方位走向法和互谱法的水中目标识别[J]. 兵工学报, 2006, 27(5): 932-935.
Liu Zhaohui, Fu Zhanping, Wang Mingzhou. Underwater target identification based on the methods of bearing and cross-spectrum[J]. Acta Armamentarii, 2006, 27(5): 932-935.
|
[6] |
石敏, 陈立纲, 蒋兴舟, 等. 具有亮点和方位延展特征的线列阵声诱饵研究[J]. 海军工程大学学报, 2005, 17(1): 58-62.
Shi Min, Chen Ligang, Jiang Xingzhou, et al. On linear array acoustic bait with light spot and azimuth-range extensi on feature[J]. Journal of Naval University of Engineering, 2005, 17(1): 58-62.
|
[7] |
樊书宏, 王英民, 岳玲, 等. 基于拖曳长线阵的水下目标模拟系统[J]. 系统仿真学报, 2012, 24(5): 1083-1087.
Fan Shuhong, Wang Yingmin, Yue Ling, et al. Underwater target simulation system based on towed long line array[J]. Journal of System Simulation, 2012, 24(5): 1083-1087.
|
[8] |
徐枫, 严冰, 王海陆, 等. 水下声自导武器垂直目标亮点高分辨算法仿真与实验研究[J]. 鱼雷技术, 2009, 17(6): 35-40.
Xu Feng, Yan Bing, Wang Hailu, et al. Simulation and experimental study high-resolution method for torpedo vertical highlights[J]. Torpedo Technology, 2009, 17(6): 35-40.
|
[9] |
Meng Q, Yang S, Piao S. The classification of underwater acoustic target signals based on wave structure and support vector machine[J]. J. Acoust. Soc. Am, 2014, 136: 2265.
|
[10] |
Chen C, Fu J. Recognition method of underwater target radiated noise based on convolutional neural network[J]. Acoust. Technol, 2019, 38: 424.
|
[11] |
Li C, Huang Z Q, Xu J, et al. Underwater target classification using deep learning[C]//Oceans 2018. Charleston, US: IEEE, 2018.
|
[12] |
Lee S, Seo I, Seok J, et al. Active sonar target classification with power-normalized cepstral coefficients and convolutional neural network[J]. Appl. Sci, 2020, 10: 8450.
|
[13] |
Kamal S, Chandran C S, Supriya M H. Passive sonar automated target classifier for shallow waters using end-to-end learnable deep convolutional LSTMs[J]. Eng. Sci. Technol. Int. J. 2021, 24: 860-871.
|
[14] |
Feng S, Zhu X. A transformer-based deep learning network for underwater acoustic target recognition[J]. IEEE Geosci. Remote. Sens. Lett, 2022, 19: 1-5.
|
[15] |
Deng J, Yang X, Liu L, et al. Real-time underwater acoustic homing weapon target recognition based on stacking technique of ensemble learning[J]. J. Mar. Sci. Eng, 2023, 11: 2305. doi: 10.3390/jmse11122305
|
[16] |
King I, Yang H Q. Ensemble learning for imbalanced-commerce transaction anomaly classification[J]. Neural Information Processing, 2009, 58(63): 866-874.
|
[17] |
Baumgartner D, Serpen G. Performance of global-local hybrid ensemble versus boosting bagging ensembles[J]. International Journal of Hybrid Intelligent Systems, 2012, 10(32): 25-37.
|
[18] |
Azizi N, Farahn N, Ennajia A. New dynamic classifiers selection approach for handwritten recognition[C]//Proc of the 22nd International Conference on Artificial Neural Networks and Machine Learning. Berlin, Germany: Springer, 2012: 189-196.
|
[19] |
Gao Y, Ji G, Yang Z, et al. A dynamic adaboost algorithm with adaptive changes of loss function[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2012, 42(6): 1828-1839.
|