基于注意力机制的堆叠LSTM网络雷达HRRP序列目标识别方法

传统的雷达高分辨距离像(high resolution range profile, HRRP)序列识别方法依赖于人工特征提取, 并且现有的深度学习方法存在梯度消失问题, 导致收敛速度慢, 识别精度低。针对上述问题, 提出一种基于注意力机制的堆叠长短时记忆(attention-based stacked long short-term memory, Attention-SLSTM)网络模型, 该模型通过堆叠多个长短时记忆(long short-term memory, LSTM)网络层, 实现了HRRP序列更深层次抽象特征的提取; 通过替换模型的激活函数, 减缓了堆叠LSTM(stacked LSTM, SLSTM)模型梯度消失问题; 引入注意力机制计算特征序列的分配权重并用于分类识别步骤, 增强了隐藏层特征的非线性表达能力。模型在雷达目标识别标准数据集MSTAR上多种不同目的的实验结果表明, 所提方法具有更快的收敛速度和更好的识别性能, 与多种现有方法对比具有更高的识别率, 证明了所提方法的正确性和有效性。

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

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.