## 4 Experiments and Results
### Incremental Recognition Performance  
The proposed method is compared with the above-mentioned state-of-the-art methods on the MSTAR dataset. In addition, the recognition accuracy of Joint Training is taken as the baseline. Among these methods, CBesIL belongs to the traditional method, while the other methods are based on deep learning. As for the setting of hyper-parameters, the exemplar size in Replay, iCaRL, and Podnet are all set to 30, which is consistent with the setting of Reference [40]. The number of atoms in the class-specific dictionary is set to 40, while the weight factors are set to 0.1 and 0.01, respectively. It should be noted that Joint Training takes Resnet18 as the backbone to extract targets' features; then, it makes use of a softmax classifier to recognize these targets. Furthermore, Joint Training adopts a batch training strategy. This means that Joint Training makes use of all training data to update the SAR ATR system when encountering data of new classes. In theory, the recognition accuracy of Joint Training should be significantly better than these incremental learning methods. We conducted experiments on the three above-mentioned scenarios. The overall recognition accuracies of these methods are presented in Table 3. Furthermore, we have visualized these accuracies in Figure 4 for a more intuitive comparison. The codes of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Scenario** & **BMP2** & **BTR70** & **T72** & **BTR60** & **2S1** & **BRDM2** & **D7** & **T62** & **ZIL131** & **ZSU234** \\ \hline
**A** & Task\_1 & & Task\_2 & Task\_3 & Task\_4 & Task\_5 & Task\_6 & Task\_7 \\
**B** & Task\_1 & & & Task\_2 & & Task\_3 & & Task\_4 \\
**C** & & Task\_1 & & & Task\_2 & & & Task\_3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Three different incremental learning scenarios for MSTAR dataset. ICAC and CBesIL have not been published, so we directly cited the experimental results of Reference [40]. The other data in Table 3 were obtained from our own experiments. It is obvious that Joint Training has the best recognition ability in the three scenarios, and its accuracies are more than 0.9917. Joint Training performs almost as well as expected, so we regard its accuracies as the benchmark. In general, with the gradual increase in classes, the recognition accuracies of these incremental learning methods basically present a downward trend. Taking Scenario_A as an example, we analyze the performance of these incremental learning methods. First, the final accuracies (accuracies of Task_7) of several incremental learning methods are 0.9592, 0.9604, 0.9559, 0.9633, 0.9176, 0.7215, and 0.9736. Compared with other incremental learning methods, the accuracies of our method are 0.0144, 0.0132, 0.0177, 0.0560 and 0.2521 higher. Second, the average accuracies of seven tasks in Replay, iCaRL, Wa, Podnet, ICAC and CBesIL are 0.9482, 0.9579, 0.9552, 0.9633, 0.9571, 0.7929. At the same time, the average accuracy of our method is 0.9767. It is obvious that our method achieves a higher average accuracy than other methods. Third, our method performs better than other incremental learning methods from Task_1 to Task_7 in Scenario_A, and it has the higher recognition accuracies in seven tasks. As for Scenario_B, first, the average recognition accuracies of four tasks in Replay, iCaRL, Wa, Podnet, ICAC, CBesIL, and our method are 0.9493, 0.9624, 0.9607, 0.9600, 0.9698, 0.8145 and 0.9766, respectively. Our method performs better on average accuracy. Second, our method has higher recognition accuracy on three tasks (Task_1, Task_3 and Task_4), and it only performs slightly worse than iCaRL on Task_3. Furthermore, after analyzing the results of Scenario_C, we can draw similar conclusions as in Scenario_B. In summary, our method has better performance on incremental SAR target recognition. It should be noted that, for each method, the recognition accuracies of Task_1 in Scenario_A, Scenario_B and Scenario_C are identical.