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- Title
基于注意力机制的堆叠LSTM网络雷达HRRP序列目标识别方法.
- Authors
张一凡; 张双辉; 刘永祥; 荆 锋
- 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.
- Subjects
RADAR targets; AUTOMATIC target recognition; HIDDEN Markov models; DEEP learning; RADAR; MACHINE learning
- Publication
Systems Engineering & Electronics, 2021, Vol 43, Issue 10, p2775
- ISSN
1001-506X
- Publication type
Article
- DOI
10.12305/j.issn.1001-506X.2021.10.09