## 1 Introduction
Specifically, POSELM applies the Particle Swarm Optimization algorithm to refine initial weights, thereby enhancing recognition accuracy [20]. Finally, [PERSON] et al. devised a multiscale incremental dictionary learning method, building upon and improving the LC-KSVD algorithm [21]. Based on self-sustainment guidance representation, [PERSON] et al. proposed an incremental SAR ATR method. This method made use of a dynamic query navigation module and a structural extension module to ensure the model's learning ability for new and old class targets [22]. [PERSON] et al. proposed a multi-stage regularization-based SAR ATR method, called SCF-CIL. SCF-CIL applied an overfitting training strategy and a multi-stage regularization method to ensure the model's incremental learning capability [23]. [PERSON] et al. proposed the \(L_{2,1}\)-constrained deep incremental NMF (\(L_{2,1}\)-DINMF) method for SAR ATR. By introducing the \(L_{2,1}\)-constraint into deep nonnegative matrix factorization (DNMF) and defining in cremental update rules, this method significantly improves the incremental recognition performance [24]. [PERSON] et al. implemented incremental SAR ATR using strong separability features (SSF-IL). The SSF-IL employs intra-class and inter-class scatter to design the feature separability loss. Additionally, a classifier bias correction method based on boundary features is devised to achieve target classification [25]. [PERSON] et al. proposed multilevel adaptive knowledge distillation network (MLAKDN) to realize incremental SAR ATR. MLAKDN proposes an adaptive weighted distillation strategy, a feature distillation method, a model rebalancing technique, and a weighted incremental classification loss to guarantee the model's incremental recognition ability [26]. Furthermore, because of the complexity and challenge of the incremental SAR ATR problem, scholars only discuss incremental learning in specific settings. For example, the newly added data in References [15; 16; 17; 18; 19; 20; 22; 23; 24; 25; 26] belong to new classes, while Reference [21] deals with the new data of existing categories. In this paper, the projects we study are the same as those in References [15; 16; 17; 18; 19; 20; 22; 23; 24; 25; 26], and they belong to class incremental learning. The above methods have good performance, but there is still room for improvement. In recent years, sparse representation has been widely used in SAR ATR, and it performed well [27; 28; 29]. The sparse representation classifier directly takes the training samples of various classes as the atoms to form a dictionary. Then, the test samples are classified according to their sparse representation under the dictionary. This approach is naturally suitable for class incremental learning. In more detail, when the samples of new classes are obtained, they can be directly used to expand the dictionary to accomplish class incremental learning. SAR target images have the characteristics of large intra-class distances and small inter-class differences. The dictionary constructed directly with training samples has difficulty adapting to this situation, and it performs poorly in SAR ATR. Therefore, we can improve this situation with a class-specific dictionary [30]. In addition, in the process of dictionary learning, regularization terms can be added to improve the recognition performance. Motivated by the aforementioned concepts, this paper employs class-specific dictionaries to realize incremental learning for SAR ATR. Initially, the objective function of dictionary learning is regularized by the intra-class divergence [31], aiming to acquire a more discriminative dictionary. Subsequently, an enhanced dictionary learning approach is utilized to derive the class-specific dictionary for each target class. Next, the sparse representations of test samples under all class-specific dictionaries are computed. Based on these class-specific dictionaries, two metrics, namely reconstruction error and intra-class divergence, are calculated.