# Lightweight Transformer Network for Ship HRRP Target Recognition
###### Abstract  
The traditional High-Resolution Range Profile (HRRP) target recognition method has difficulty automatically extracting target deep features, and has low recognition accuracy under low training samples. To solve these problems, a ship recognition method is proposed based on the lightweight Transformer model. The model enhances the representation of key features by embedding Recurrent Neural Networks (RNN) into Transformer's encoder. The Group Linear Transformations (GLTs) are introduced into Transformer to reduce the number of parameters in the model, and stable features are extracted through linear intergroup dimensional transformations. The adaptive gradient clipping algorithm is combined with the Stochastic Gradient Descent (SGD) optimizer to allow the gradient to change dynamically with the training process and to improve the training speed and generalization ability of the model. Experimental results on the simulated dataset show that multi-layer model stacking can effectively extract deep features of targets and raise recognition accuracy. At the same time, the lightweight Transformer model can maintain good recognition performance with low parameters and low training samples.  
Radar Automatic Target Recognition (RATR); High-Resolution Range Profile (HRRP); Recurrent Neural Network (RNN); attention model; lightweight model +
Footnote †: journal:  
for recognition, and the experimental results demonstrated that the use of CNN could effectively improve the recognition rate of ship targets.  
All the above models ignore the fact that HRRP signals are temporal signals, and in order to make better use of the temporal dependence in HRRP, [PERSON] et al. [14] applied recurrent neural networks (RNN) to HRRP recognition and achieved better results under small sample conditions. However, the RNN was also found to suffer from the problem of gradient vanishing. To solve this problem, [PERSON] et al. [15] proposed a VGM-RNN model to extrapolate missing samples in the sequence, which not only effectively alleviates the problem of gradient vanishing, but also has excellent noise immunity. The literature [16; 17; 18] proposes to combine the attention mechanism with RNN, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). According to the importance of the implicit state features in target recognition, weights are assigned to the implicit state features encoded by RNN, LSTM and GRU to make the extracted features more discriminative. [PERSON] et al. [19] combined RNN with a deep clustering mechanism, using region decomposition and attention mechanism to accumulate each region weight, while optimizing the cross-entropy loss function to make the model more robust and recognition performance better. The literature [20] used attention modules and feedforward neural units to construct Transformer networks, and extract deep features by stacking attention modules and feedforward neural units [21; 22].  
This paper improves the encoding part of the Transformer model and proposes a lightweight Transformer model. The model is based on the Transformer encoders and introduces a local RNN module [23] at the input of each encoder to extract correlations between neighbouring distance units, and combines the global correlations extracted by the Transformer encoders to enhance the representation of feature. At the same time, intergroup dimensional transformations and feature interactions by GLTs [24] are used to fully extract the target features and reduce parameters in the model. The use of adaptive gradient clipping algorithm [25] in the SGD optimizer allows the gradient descent process to vary dynamically with model training, speeding up model training process and enhancing the stability of model.