基于深度学习的弹道目标智能分类

doi:10.3969/j.issn.1001-506X.2020.06.04

Abstract

Abstract:

Aiming at the problems of translational compensation before micro-motion classification of ballistic targets and the need to construct artificial features for typical radar cross-section(RCS) sequence classification, an intelligent classification method of ballistic targets based on micro-motion characteristics of ballistic targets and RCS is proposed. Firstly, the ballistic targets motion model is established and the azimuth and elevation angles are analyzed to obtain the RCS sequence. On this basis, the time-frequency diagram is obtained by using wavelet transform to construct the data set. Then, the time-frequency diagram feature sequence is extracted by convolutional neural network(CNN) and fused with the RCS sequence to form high-dimensional features. Finally, the bidirectional long short-term memory network with fault tolerance is used to fully learn the correlation between sequences to achieve target classification. The simulation results show that the classification accuracy of the proposed algorithm is 5% and 2% higher than that of CNN and support vector machines, and the classification speed is 1.5 and 2.5 times faster than that of CNN and bidirectional long short-term memory networks, respectively. The algorithm achieves faster intelligent classification with higher accuracy.

Key words: deep learning, ballistic target, intelligent classification, radar cross-section(RCS), wavelet transform

CLC Number:

TN957

Jiang LI, Cunqian FENG, Yizhe WANG, Sisan HE. Intelligent classification of ballistic targets based on deep learning[J]. Systems Engineering and Electronics, 2020, 42(6): 1226-1234.

Figures/Tables 17

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参数名称	参数设置
参数名称	BiLSTM	CNN
ExecutionEnvironment	GPU	GPU
ValidationFrequency	20	20
GradientThreshold	2	2
numHiddenUnits	200	-
InitialLearnRate	0.000 1	0.000 1
MiniBatchSize	50	50
SolverName	RMSProp	Adam
-	-	-

分类方法	BiLSTM	SVM	Bayes	分类决策树
准确性/%	99.63	97.22	92.41	91.30

迭代次数	AlexNet		ResNet-18
迭代次数	训练时间/s	识别率/%	训练时间/s	识别率/%
2	56	88.70	59	91.48
5	128	92.04	139	95.18
8	198	93.89	230	94.63
10	243	93.22	275	94.81

网络模型	迭代次数	训练时间/s	仿真时间/s	识别率/%
ResNet-18	6	171	202	90.56
BiLSTM	130	345	349	97.41
本文算法	45	105	137	98.07

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Intelligent classification of ballistic targets based on deep learning

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