基于集成深度学习的有源干扰智能分类

doi:10.12305/j.issn.1001-506X.2022.12.02

Abstract

Abstract:

Aiming at the problems that most existing machine learning-based classification of radar active jamming need to construct artificial feature sets and low classification accuracy in the case of small samples, an ensemble convolutional neural network (CNN) classification method based on multichannel feature fusion is proposed. Firstly, multiple mathematical models of active jamming are established, simulated and the corresponding time-frequency profiles are obtained by using the short-time Fourier transform (STFT). Secondly, real part, imaginary part, and modulus three-channel features of time-frequency distribution plots are extracted to establish sample sets containing different combinations of features through multiple feature combinations. Ultimately, an ensemble depth model with CNN as the base classifier is constructed, each CNN separately extracts the features of different sample sets, and majority voting on the prediction results of all base classifiers gives the overall prediction results of the ensemble model. The experiments show that the proposed method can effectively realize highly accurate intelligent identification of multiple classes of active jamming in the case of small samples.

Key words: active jamming classification, short-time Fourier transform (STFT), ensemble learning, convolutional neural network (CNN), small sample

CLC Number:

TN974

Qinzhe LYU, Yinghui QUAN, Minghui SHA, Shuxian DONG, Mengdao XING. Ensemble deep learning-based intelligent classification of active jamming[J]. Systems Engineering and Electronics, 2022, 44(12): 3595-3602.

Figures/Tables 8

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References 30

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干扰类型	参数类型	取值范围
DFTJ	假目标数/个	3~6
DFTJ	假目标延迟/μs	1~10
ISRJ+SNJ	转发次数/次	1~4
	干扰占空比/%	20~50
	采样周期/μs	5, 10
RDJ	假目标延迟/μs	1~10
SJ	干扰带宽/MHz	20~40
BJ	干扰带宽/MHz	50~80
LSJ	扫频周期μs	40~80
LSJ	扫频带宽/MHz	20

网络层	卷积核	最大池化	激活函数
输入层		N×(247×100×3)
卷积层1	(13×13)×64	2×2	ReLU
正则化层		Dropout(0.25)
卷积层2	(9×9)×128	2×2	ReLU
卷积层3	(5×5)×128	2×2	ReLU
正则化层		Dropout(0.25)
卷积层4	(3×3)×256	2×2	ReLU
Flatten层		1×5 632
全连接层	-	-	Softmax
输出层		1×12

网络层	卷积核	最大池化	激活函数
输入层		N×(2 000×3)
卷积层1	(16×1)×16	2×1	ReLU
正则化层		Dropout(0.25)
卷积层2	(32×1)×32	2×1	ReLU
卷积层3	(64×1)×64	2×1	ReLU
正则化层		Dropout(0.25)
卷积层4	(128×1)×128	2×1	ReLU
Flatten层		1×5 120
全连接层	-	-	Softmax
输出层		1×12

干扰	算法
干扰	时域 CNN	人工特征提取+RF	时频域 CNN	所提 ECNN
TG	88.55±8.80	94.11±3.21	97.32±4.50	99.75±0.24
DFTJ	92.08±3.29	86.10±4.36	92.77±4.34	98.72±0.59
ISRJ	78.15±10.62	98.50±3.47	95.43±3.39	93.46±1.12
RDJ	76.86±13.34	97.32±3.78	95.80±2.00	98.59±0.21
SNJ	76.46±9.86	92.49±2.61	94.89±1.18	83.61±1.32
SJ	99.50±0.74	99.79±0.25	98.24±2.15	99.14±0.55
BJ	92.30±14.52	88.18±5.37	96.33±3.19	100.00±0.00
LSJ	70.47±9.85	87.31±3.82	83.92±7.56	89.23±1.57
RDJ+SNJ	78.95±12.70	90.32±1.65	86.54±7.19	90.77±1.34
DFTJ+SNJ	97.66±2.22	89.55±4.76	96.14±3.19	100.00±0.00
RDJ+ISRJ	83.40±8.96	95.57±1.99	94.50±3.62	96.73±0.91
LSJ+ISRJ	66.04±7.96	89.91±4.54	85.32±10.33	92.90±4.72

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Ensemble deep learning-based intelligent classification of active jamming

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