基于多核稀疏保持投影的多特征集典型相关分析的水下目标特征融合方法
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摘要
针对水下目标识别特征样本集高维小样本问题,提出了基于多核稀疏保持投影的多特征集典型相关分析的水下目标特征融合方法。该方法用多特征集典型相关分析算法对多域特征的整体相关程度进行定量分析,去除冗余和噪声特征,实现多域特征的融合,并利用多核稀疏保持投影算法,对提取的多域特征样本的稀疏重构性加以约束,增强了特征的判别能力。利用实测舰船辐射噪声数据验证基于核稀疏保持投影的多特征集典型相关分析的水下目标特征融合方法的有效性,与多特征集典型相关分析方法和核稀疏保持投影典型相关分析方法进行了对比,实验研究表明,提出的方法可以有效去除冗余和噪声特征,实现多域水下目标特征的融合,提高水下目标的识别正确率。
To solve high-dimensional and small-sample-size classification problem for underwater target recognition,a new feature fusion method is proposed based on multi-kernel sparsity preserve multi-set canonical correlation analysis. The multi-set canonical correlation analysis algorithm is used to quantitatively analyze the correlation of multidomain features,remove redundant and noise features,in order to achieve multi-domain feature fusion. The multikernel sparsely preserved projection algorithm is used to constrain the sparse reconstruction of the extracted multidomain feature samples,which enhances the feature's classification ability. Results of applying real radiated noise datasets to underwater target recognition experiments show that our new method can effectively remove the redundancy and noise features, achieve the fusion of multi-domain underwater target features, and improve the recognition accuracy of underwater targets.
To solve high-dimensional and small-sample-size classification problem for underwater target recognition,a new feature fusion method is proposed based on multi-kernel sparsity preserve multi-set canonical correlation analysis. The multi-set canonical correlation analysis algorithm is used to quantitatively analyze the correlation of multidomain features,remove redundant and noise features,in order to achieve multi-domain feature fusion. The multikernel sparsely preserved projection algorithm is used to constrain the sparse reconstruction of the extracted multidomain feature samples,which enhances the feature's classification ability. Results of applying real radiated noise datasets to underwater target recognition experiments show that our new method can effectively remove the redundancy and noise features, achieve the fusion of multi-domain underwater target features, and improve the recognition accuracy of underwater targets.
引文
[1] MENG Q,YANG S,PIAO S. The classification of Underwater Acoustic Target Signals Based on Wave Structure and Support Vector Machine[J]. Journal of the Acoustical Society of America,2014,136(4):2265-2265
[2]程玉胜,张宝华,高鑫,等.船舶辐射噪声解调谱相位耦合特性与应用[J].声学学报,2012(1):25-29CHENG Yusheng,ZHANG Baohua,GAO Xing,et al. Phase-Coupling Characteristics of Ship Radiiated-Noise Demodulation Spectrum And Application[J]. Acta Acustica,2012(1):25-29(in Chinese)
[3] JIAO Y M,KANG C Y,ZENG X X,et al. Extraction and Application in Nonlinear Spectrum Feature of Ship Radiated Noise[J]. Ship Science&Technology,2016,38(12):65-68
[4] SLAMNOIU G,RADU O,ROSCA V,et al. DEMON-Type Algorithms for Determination of Hydro-Acoustic Signatures of Surface Ships and of Divers[C]∥Materials Science and Engineering Conference Series,2016
[5] LIU Y,ZHANG X,SHAO J. Quadratic Time-Frequency Feature Extraction and Fusion for Ship Targets Classification[C]∥International Conference on Signal Processing,2015
[6] CAO H L,FANG S L,LUO X W. Nonlinear Feature Extraction and Recognition of Ship Radiated Noise[J]. Journal of Nanjing University,2013,49(1):64-71
[7] YANG H,SHEN S,YAO X,et al. Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition[J].Sensors,2018,18(4):952
[8] SHEN S,YANG H,SHENG M. Compression of a Deep Competitive Network Based on Mutual Information for Underwater Acoustic Targets Recognition[J]. Entropy,2018,20(4):243
[9]袁帅,杨宏晖,申星.基于互信息的顺序向前特征选择算法[J].声学技术,2014(4):359-362YUAN Shuai,YANG Honghui,SHEN Xing. Forward Order Feature Selection Algorithm Based on Mutual Information[J]. Technical Acoustics,2014(4):359-362(in Chinese)
[10]马超,陈西宏,徐宇亮,等.广义邻域粗集下的集成特征选择及其选择性集成算法[J].西安交通大学学报,2011,45(6):34-39MA Chao,CHEN Xihong,XU Yuliang,et al. Ensemble Feature Selection Based on Generalized Neighborhood Rough Model and Its Selective Integration[J]. Journal of Xi'an Jiaotong University,2011,45(6):34-39(in Chinese)
[11]杨宏晖,戴健,孙进才,等.用于水声目标识别的自适应免疫特征选择算法[J].西安交通大学学报,2011,45(12):28-32YANG Honghui,DAI Jian,SUN Jincai,et al. A New Adaptive Immune Feature Selection Algorithm for Underwater Acoustic Target Classification[J]. Journal of Xi'an Jiaotong University,2011,45(12):28-32(in Chinese)
[12]柳俊峰,章新华,许林周.动态规划算法在被动声呐目标检测中的应用[J].舰船科学技术,2012,34(3):95-98LIU Junfeng,ZHANG Xinhua,XU Linzhou. Application of Dynamic Programming in Passive Sonar for Detecting Target[J].Ship Science&Technology,2012,34(3):95-98(in Chinese)
[2]程玉胜,张宝华,高鑫,等.船舶辐射噪声解调谱相位耦合特性与应用[J].声学学报,2012(1):25-29CHENG Yusheng,ZHANG Baohua,GAO Xing,et al. Phase-Coupling Characteristics of Ship Radiiated-Noise Demodulation Spectrum And Application[J]. Acta Acustica,2012(1):25-29(in Chinese)
[3] JIAO Y M,KANG C Y,ZENG X X,et al. Extraction and Application in Nonlinear Spectrum Feature of Ship Radiated Noise[J]. Ship Science&Technology,2016,38(12):65-68
[4] SLAMNOIU G,RADU O,ROSCA V,et al. DEMON-Type Algorithms for Determination of Hydro-Acoustic Signatures of Surface Ships and of Divers[C]∥Materials Science and Engineering Conference Series,2016
[5] LIU Y,ZHANG X,SHAO J. Quadratic Time-Frequency Feature Extraction and Fusion for Ship Targets Classification[C]∥International Conference on Signal Processing,2015
[6] CAO H L,FANG S L,LUO X W. Nonlinear Feature Extraction and Recognition of Ship Radiated Noise[J]. Journal of Nanjing University,2013,49(1):64-71
[7] YANG H,SHEN S,YAO X,et al. Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition[J].Sensors,2018,18(4):952
[8] SHEN S,YANG H,SHENG M. Compression of a Deep Competitive Network Based on Mutual Information for Underwater Acoustic Targets Recognition[J]. Entropy,2018,20(4):243
[9]袁帅,杨宏晖,申星.基于互信息的顺序向前特征选择算法[J].声学技术,2014(4):359-362YUAN Shuai,YANG Honghui,SHEN Xing. Forward Order Feature Selection Algorithm Based on Mutual Information[J]. Technical Acoustics,2014(4):359-362(in Chinese)
[10]马超,陈西宏,徐宇亮,等.广义邻域粗集下的集成特征选择及其选择性集成算法[J].西安交通大学学报,2011,45(6):34-39MA Chao,CHEN Xihong,XU Yuliang,et al. Ensemble Feature Selection Based on Generalized Neighborhood Rough Model and Its Selective Integration[J]. Journal of Xi'an Jiaotong University,2011,45(6):34-39(in Chinese)
[11]杨宏晖,戴健,孙进才,等.用于水声目标识别的自适应免疫特征选择算法[J].西安交通大学学报,2011,45(12):28-32YANG Honghui,DAI Jian,SUN Jincai,et al. A New Adaptive Immune Feature Selection Algorithm for Underwater Acoustic Target Classification[J]. Journal of Xi'an Jiaotong University,2011,45(12):28-32(in Chinese)
[12]柳俊峰,章新华,许林周.动态规划算法在被动声呐目标检测中的应用[J].舰船科学技术,2012,34(3):95-98LIU Junfeng,ZHANG Xinhua,XU Linzhou. Application of Dynamic Programming in Passive Sonar for Detecting Target[J].Ship Science&Technology,2012,34(3):95-98(in Chinese)
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