一种改进的DNN算法在雷达信号分选中的应用
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摘要
针对深度神经网络能自动学习数据深层特征的优点进行了研究,提出一种基于深度信念网络的信号分选方法,来解决传统雷达信号分选中人工提取特征的耗时、特征冗余等问题。通过堆叠多层的深度模型对原算法进行改进,克服单一模型学习力的不足,对不同信号的本质特征进行深入学习,融合各个深度模型的后验概率进行分类决策,从而进一步提高了信号的识别率。采用改进方法对七种不同类型的雷达信号进行分选识别,并与其他信号分选方法进行对比。实验结果表明,该方法取得了更好的分类效果,展现出较强的学习数据本质特征的能力,从而验证了算法的有效性和优越性。
The advantage of deep neural network to automatically learn the deep characteristics of data was studied. This paper proposed a signal sorting method based on multilayer deep belief networks,in order to solve the problems of time consuming in traditional radar signal selection,feature redundancy and so on. Based on the improved algorithm of depth of stacked multilayer model,it overcame the problem of insufficient to the single model of learning ability,and deeply studied the essential features of the different signal,and fused the posterior probability of the model to make a classification decision,so as to further improve the signal recognition rate. It used this method to sort 7 different types of radar emitter signal sorting. Compared with other performance signal sorting method,the experimental results show that this method obtains better classification results,and exhibits strong learning ability to nature features,thus it verifies the effectiveness and superiority of this algorithm.
The advantage of deep neural network to automatically learn the deep characteristics of data was studied. This paper proposed a signal sorting method based on multilayer deep belief networks,in order to solve the problems of time consuming in traditional radar signal selection,feature redundancy and so on. Based on the improved algorithm of depth of stacked multilayer model,it overcame the problem of insufficient to the single model of learning ability,and deeply studied the essential features of the different signal,and fused the posterior probability of the model to make a classification decision,so as to further improve the signal recognition rate. It used this method to sort 7 different types of radar emitter signal sorting. Compared with other performance signal sorting method,the experimental results show that this method obtains better classification results,and exhibits strong learning ability to nature features,thus it verifies the effectiveness and superiority of this algorithm.
引文
[1]Salakhutdinov R,Hinton G E.Deep Boltzmann machines[C]//Proc of the 12th International Conference on Artificial Intelligence and Statistics.2009:448-455.
[2]张葛祥,荣海娜,金炜东.基于小波包变换和特征选择的雷达辐射源信号识别[J].电路与系统学报,2006,11(6):45-49,55.(Zhang Gexiang,Rong Haina,Jin Weidong.Identification of radar emitter signals based on wavelet packet transform and feature selection[J].Journal of Circuits and Systems,2006,11(6):45-49,55.)
[3]余志斌.基于脉内特征的雷达辐射源信号识别研究[D].成都:西南交通大学,2010.(Yu Zhibin.Study on radar emitter signal identification based on intra-pulse features[D].Chengdu:Southwest Jiaotong University,2010.)
[4]Zhou Zhiwen,Huang Gaoming,Gao Jun,et al.A deep learning algorithm for radar emitter recognition[J].Journal of Xi’an Electronic and Science University,2017,44(3):85-90.
[5]Jurgen S.Deep learning in neural networks:an overview[J].Neural Networks,2015,61(1):85-117.
[6]Hinton G,Salakhutdinov R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507.
[7]Sarikya R,Hinton G,Deoras A.Application of deep belief networks for natural language understanding[J].IEEE Trans on Audio,Speech,and Language Processing,2014,22(4):778-784.
[8]Welling M,Hinton G E.A new learning algorithm for mean field Boltzmann machines[C]//Proc of International Conference on Artificial Neural Networks.Berlin:Springer,2002:351-357.
[9]Bengio Y,Lamblin P,Popovici D.Greed layer-wise training of deep networks[C]//Advances in Neural Information Processing Systems.2007:153-160.
[10]Deng Li,Yu Dong,Platt J.Scalable stacking and learning for building deep architectures[C]//Proc of IEEE International Conference on Acoustics,Speech and Signal Processing.Piscataway,NJ:IEEEPress,2012.
[11]Mohamed A,Dahl G E,Hinton G.Acoustic modeling using deep belief networks[J].IEEE Trans on Audio,Speech and Language Processing,2012,20(1):14-22.
[12]Sun Zhijun,Xue Lei,Xu Yang.Feature extraction algorithm for marginal Fisher analysis based on deep learning[J].Journal of Electronics and Information,2013,35(4):805-811.
[13]Hinton G.Deep neural networks for acoustic modeling in speech recognition:the shared views of four research groups[J].IEEE Signal Processing Magazine,2012,29(6):82-97.
[14]Qiu Xueheng,Zhang Le,Ponnuthurai N,et al.Ensemble deep learning for regression and time series forecasting[C]//Proc of IEEESymposium on Computational Intelligence in Ensemble Learning.Piscataway,NJ:IEEE Press,2014:122-126.
[15]Chamasemani F F,Singh Y P.Multi-class support vector machine(SVM)classifiers-an application in bioinspired computing:theories and applications(BIC-TA)[C]//Proc of the 6th International Conference on Bio-Inspired Computing:Theories and Applications.Piscataway,NJ:IEEE Press,2011:351-356.
[2]张葛祥,荣海娜,金炜东.基于小波包变换和特征选择的雷达辐射源信号识别[J].电路与系统学报,2006,11(6):45-49,55.(Zhang Gexiang,Rong Haina,Jin Weidong.Identification of radar emitter signals based on wavelet packet transform and feature selection[J].Journal of Circuits and Systems,2006,11(6):45-49,55.)
[3]余志斌.基于脉内特征的雷达辐射源信号识别研究[D].成都:西南交通大学,2010.(Yu Zhibin.Study on radar emitter signal identification based on intra-pulse features[D].Chengdu:Southwest Jiaotong University,2010.)
[4]Zhou Zhiwen,Huang Gaoming,Gao Jun,et al.A deep learning algorithm for radar emitter recognition[J].Journal of Xi’an Electronic and Science University,2017,44(3):85-90.
[5]Jurgen S.Deep learning in neural networks:an overview[J].Neural Networks,2015,61(1):85-117.
[6]Hinton G,Salakhutdinov R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507.
[7]Sarikya R,Hinton G,Deoras A.Application of deep belief networks for natural language understanding[J].IEEE Trans on Audio,Speech,and Language Processing,2014,22(4):778-784.
[8]Welling M,Hinton G E.A new learning algorithm for mean field Boltzmann machines[C]//Proc of International Conference on Artificial Neural Networks.Berlin:Springer,2002:351-357.
[9]Bengio Y,Lamblin P,Popovici D.Greed layer-wise training of deep networks[C]//Advances in Neural Information Processing Systems.2007:153-160.
[10]Deng Li,Yu Dong,Platt J.Scalable stacking and learning for building deep architectures[C]//Proc of IEEE International Conference on Acoustics,Speech and Signal Processing.Piscataway,NJ:IEEEPress,2012.
[11]Mohamed A,Dahl G E,Hinton G.Acoustic modeling using deep belief networks[J].IEEE Trans on Audio,Speech and Language Processing,2012,20(1):14-22.
[12]Sun Zhijun,Xue Lei,Xu Yang.Feature extraction algorithm for marginal Fisher analysis based on deep learning[J].Journal of Electronics and Information,2013,35(4):805-811.
[13]Hinton G.Deep neural networks for acoustic modeling in speech recognition:the shared views of four research groups[J].IEEE Signal Processing Magazine,2012,29(6):82-97.
[14]Qiu Xueheng,Zhang Le,Ponnuthurai N,et al.Ensemble deep learning for regression and time series forecasting[C]//Proc of IEEESymposium on Computational Intelligence in Ensemble Learning.Piscataway,NJ:IEEE Press,2014:122-126.
[15]Chamasemani F F,Singh Y P.Multi-class support vector machine(SVM)classifiers-an application in bioinspired computing:theories and applications(BIC-TA)[C]//Proc of the 6th International Conference on Bio-Inspired Computing:Theories and Applications.Piscataway,NJ:IEEE Press,2011:351-356.