## 1 Introduction  
With the advantages of synthetic aperture radar (SAR), including all-day, all-weather, and long operation range [1], it is widely used to identify and monitor targets of concern [2, 3, 4]. In this domain, SAR target recognition methods aim to automatically identify the target within the image patch. To date, deep learning has demonstrated its remarkable capabilities in image processing applications [4], owing to its exceptional learning performance. With the development of deep learning, the application of deep neural networks (DNNs) in remote sensing target recognition is becoming increasingly popular [5, 6, 7, 8, 9, 10, 11]. These recognition methods realize outstanding performance in SAR target recognition on some pre-constructed fixed dataset. However, in some certain application scenarios for SAR target classification, the radar system needs to continuously update the network's functionality with newly collected data. The traditional network-based target recognition methods are fixed after training. Whenthe network is adjusted using only newly collected data, it may suffer from catastrophic forgetting [12; 13; 14]; in other words, the network might forget the previously learned knowledge while training on the new data. To maintain recognition performance on the old classes, the entire network needs to be retrained with all the data whenever new data are acquired. However, storing all the data and retraining the network is storage-consuming, time-consuming, and computationally expensive. Therefore, standard conventional classification methods struggle to meet the demands of applications requiring continuous learning of new categories. In contrast, class-incremental learning (CIL) methods enable the model to progressively obtain new recognition abilities by processing new data arriving in batches, while retaining previously learned knowledge. This approach is well-suited for addressing the problem mentioned above [15; 16; 17; 18]. Therefore, to improve the applicability and sustainability of SAR target classification methods, this paper focuses on applying CIL methods to SAR targets. Figure 1 illustrates the main process of CIL. At stage \(i\), the network is able to distinguish the instances of classes 1-\(n-1\), where \(n=\sum s_{i}+1\). Then, at stage \(i+1\), the training data in stage \(i+1\) with \(s_{i+1}\) classes are collected for network updating. The CIL method requires the network to recognize the new classes while retaining the ability to recognize the former \(n-1\) classes. One thing to note is that the network usually does not have access to all the former data, for which there is a serious sample imbalance problem when training the network. Facing this problem, directly expanding the dimension of the classifier to increase the number of network classifier categories causes the network to tend to recognize the instances of the former classes as those of the current classes after training--in other words, forgetting the knowledge of the former classes, a phenomenon known as catastrophic forgetting [12; 13]. This problem was first described by [PERSON] and [PERSON] [14], who systematically analyzed the catastrophic interference in sequential learning scenarios. To solve the problem of catastrophic forgetting in CIL, many researchers have made numerous efforts. iCaRL [19] calculated the average feature vectors for new and former classes using all instances of the new classes and a subset of instances from the former classes. It performed classification using the Nearest-Mean-of-Exemplars method, which minimized the parameters updated during the CIL process. Combining classification loss and distillation loss, iCaRL can release the influence caused by catastrophic forgetting. However, iCaRL tends to be overfitting to the former classes.