基于注意力机制的堆叠LSTM网络雷达HRRP序列目标识别方法

doi:10.12305/j.issn.1001-506X.2021.10.09

Abstract

Abstract:

The traditional radar high resolution range profile (HRRP) sequence recognition method relies on artificial feature extraction, and the existing deep learning method has the problem of gradient vanishing, which leads to the slow convergence speed and low recognition accuracy of the existing recognition methods. To solve these problems, an attention-based stacked long short-term memory (Attention-SLSTM) network model is proposed, which realizes the extraction of deeper abstract features of HRRP sequence by stacking multiple long short-term memory (LSTM) network layers.By replacing the activation function of the model, it slows down the gradient vanishing problem of stacked LSTM.The attention mechanism is introduced to calculate the distribution weight of feature sequence and use it in the classification and recognition step, which enhances the nonlinear expression ability of hidden layer features. Experimental results on the radar target recongnition standard data set MSTAR for different purposes show that the proposed method has faster convergence speed and better recognition performance, and has higher recognition rate compared with other existing methods, which proves the correctness and effectiveness of the proposed method.

Key words: high-resolution range profile sequence (HRRPs), attention mechanism, long short-term memory (LSTM) network, radar automatic target recognition (RATR)

CLC Number:

TN95

Yifan ZHANG, Shuanghui ZHANG, Yongxiang LIU, Feng JING. Radar HRRP sequence target recognition method of attention mechanism based stacked LSTM network[J]. Systems Engineering and Electronics, 2021, 43(10): 2775-2781.

Figures/Tables 11

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References 31

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类别	训练集(17°)	测试集(15°)
BMP2(SN-9566)	2 330	1 960
BTR70(SN-C71)	2 330	1 960
T72(SN-132)	2 320	1 960
BTR60	2 560	1 950
2S1	2 990	2 740
BRDM2	2 980	2 740
D7	2 990	2 740
T62	2 990	2 730
ZIL131	2 990	2 740
ZSU23/4	2 990	2 740
合计	27 470	32 030

层数	模型	识别率
1	SVM	0.820 0
1	RNN	0.867 0
1	LSTM	0.882 4
1	SLSTM-sigmoid	0.890 9
1	SLSTM-Elu	0.905 4
1	Attention-RNN	0.910 5
1	Attention-SLSTM	0.948 0

层数	SLSTM- sigmoid	SLSTM- Elu	Attention- RNN	Attention- SLSTM
1	0.890 9	0.905 4	0.910 5	0.948 0
2	0.918 9	0.925 5	0.929 4	0.952 7
3	0.928 2	0.936 0	0.942 2	0.969 0
4	0.902 2	0.943 4	0.954 6	0.972 4
5	0.887 7	0.950 2	0.960 0	0.978 6
6	0.882 4	0.954 3	0.958 3	0.980 0
7	0.880 0	0.942 2	0.952 5	0.980 3
8	0.872 5	0.903 0	0.952 1	0.978 8
9	0.868 0	0.892 0	0.951 5	0.978 5
10	0.865 4	0.880 5	0.952 0	0.979 0

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Radar HRRP sequence target recognition method of attention mechanism based stacked LSTM network

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