## 6 Conclusions  
In this paper, we propose a class-incremental learning method called SCF-CIL. By incorporating a cross-attention mechanism, our SCF-Net effectively leverages the electromagnetic information contained in SAR data, while combining the stability of SC features  
Figure 16: Angular changes in training process. (**a**) Angular changes between old-class sample features and their corresponding classifier weight vectors during training. (**b**) Angular changes between old-class sample features and non-corresponding classifier weight vectors (i.e., weights of other classes) during training.  
Figure 17: Experiment on our AC loss.  
with the plasticity of CNN features, improving the performance of CIL methods on SAR data. A comparison experiment shows that fusing the SC feature with cross-attention greatly improves the CIL performance on SAR targets. Afterward, the effect of network overfitting on the feature space is analyzed, and two training strategies, \"removing dropout\" and \"late-stop\", are introduced to improve feature clustering degree, thereby reserving feature space for subsequent incremental learning in our specific model setting. Four groups of experiments are conducted to demonstrate the impact of overfitting on feature clustering and the improvements in CIL performance due to slight overfitting. Since the SC feature is manually extracted, and in order to further correct the former class recognition bias caused by sample imbalance, a multi-stage regularization method is proposed. Through regularization at different stages, it transfers the impact of catastrophic forgetting to vector modules that are easier to process. In this part, this paper proposes an AC loss, which constrains the angle between the new feature vector and the old classifier weight in the CIL training process. Our experiment also proves the effectiveness of the AC loss. In addition to that, we compare our SCF-CIL with six commonly used CIL methods. Our method achieves the optimal CIL performance in the five stages of the CIL process. In summary, the SCF-CIL method has good effectiveness and robustness in SAR targets CIL process and great potential in the application of an SAR recognition system.  
Our experiments show the effectiveness of utilizing the information contained in SAR images and demonstrate that there is still room for improvement in the performance of CIL methods on SAR targets. However, the extraction and integration of SAR image information in our current work remain insufficient. In the future, we will focus on an end-to-end incremental learning network and improve our feature fusion method to enhance the intelligence of radar systems.  
The contribution of authors is stated as follows: methodology and formulation, Y.Z. and J.Z.; software realization, Y.Z.; validation and experiments, Y.Z.; writing and review, Y.Z., J.Z. and S.V.; funding acquisition, M.X. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Key Program of the National Natural Science Foundation of China under Grant 62331020, the National Natural Science Foundation of China under Grant 62301403, and China Scholarship Council program (Project ID: 202306960013).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.