## 5 Conclusions  
Due to the high cost of underwater target data acquisition, a Siamese network for identifying DEMON spectra of underwater targets was developed during our study and reported in this paper. We used the simulation data to train and evaluate the network performance and then used the simulating and real samples to test the network performance; then, we added more real samples to evaluate the network performance. The proposed network recognized targets accurately from a small sample. During the training process, the parameters of the Siamese network were tuned by evaluating datasets with different Doppler shifts, SNRs, and interference to obtain a network with good generalization performance. The article did not study the underwater target radiation noise deeply enough, and the difference between the virtual sample obtained from simulation and the real sample will cause the network recognition performance to be affected. The research of underwater target radiation noise modeling should be strengthened to reduce the differences between the simulated samples and the real samples. On the other hand, the number of real target samples increased in the network training process to improve the network recognition performance. The experimental results showed that a network trained on samples without a Doppler shift and interference has a high Doppler tolerance and can recognize underwater targets with different SNRs and levels of interference. The cross-entropy contrastive loss function enabled the differences between input sample pairs to be determined accurately so that targets can be recognized from small samples. Convolutional neural networks need a large number of training samples to achieve the ideal recognition effect, although the twin network-based small sample UATR algorithm effectively improves the recognition accuracy of the algorithm in the case of insufficient training samples; however, improving the recognition rate of the UATR algorithm in the case of very few underwater target samples or even a single sample is the next problem to be seriously considered.  
The approach mentioned in this paper differs from the traditional approach of using a large number of multi-state samples to train the network and then to identify multi-state samples. In this paper, we used single-state samples to train the network, and the results showed that we can better identify multi-state samples. In this paper, more simulation samples were used for evaluation and fewer real samples were used for testing. For our next study, we will evaluate the network with more real samples so that the new real samples will be identified with better results. If necessary, all simulation samples for training and evaluation will be replaced with real samples. This study needs to be completed on the basis of subsequent collection of a sufficient amount of real data. In addition, the cpu-based UATR is slow, and we will port the network model to FPGA for accelerated recognition. The training and recognition rate of the network model for a large number of samples can be greatly improved by the ARM architecture.  
Figure 10: The classification results of samples with number = 2.  
**Author Contributions:** Conceptualization, D.L.; methodology, D.L., W.S. and W.C.; software, formal analysis, W.H., B.W. and W.S.; validation, W.S. and W.C.; writing--original draft preparation, W.H. and B.W.; writing--review and editing, W.S. and W.C.; supervision, D.L.; project administration, D.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Program of Tianjin, grant number 21 YDTJPJC00180, and the Science and Technology Program of Hebei, grant number 22350901D.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.