Deep neural network (DNN)-based synthetic aperture radar automatic target recognition (SAR ATR) methods have made great progress in recent years. However, the performance of DNN models relies on a large number of independent and identically distributed measured synthetic aperture radar (SAR) images, which is contrary to the SAR ATR in practice. Furthermore, DNN models also suffer from catastrophic forgetting when learning a sequence of new classes. To tackle these problems, we introduce simulated data into the class incremental learning of SAR ATR for the first time. Specifically, we aim to continuously learn a sequence of new classes with a small amount of measured data and a large amount of simulated data. We first investigate the properties of incremental learning using simulated data, and the main observation is that simulated data can achieve good performance in short-term incremental learning rather than long-term incremental learning. A novel class incremental learning method, namely, feature guided evolution and distillation (FGED), is then presented. On the one hand, FGED encourages simulated data to have the same feature relationship structure as the corresponding measured data to reduce their distribution discrepancy in short-term incremental learning. On the other hand, FGED adopts a feature distillation strategy to simultaneously reduce the distribution discrepancy accumulation of previous incremental classes and alleviate the catastrophic forgetting in long-term incremental learning. The experimental results obtained on the MSTAR benchmark dataset and two simulated datasets demonstrate the effectiveness of FGED.

中文翻译：

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增量 SAR ATR 的模拟数据特征引导演化和蒸馏


基于深度神经网络（DNN）的合成孔径雷达自动目标识别（SAR ATR）方法近年来取得了长足的进步。然而，DNN模型的性能依赖于大量独立且同分布的测量合成孔径雷达（SAR）图像，这与实践中的SAR ATR是相反的。此外，DNN 模型在学习一系列新类别时也会遭受灾难性遗忘。为了解决这些问题，我们首次将模拟数据引入SAR ATR的增量学习类中。具体来说，我们的目标是利用少量测量数据和大量模拟数据不断学习一系列新类。我们首先使用模拟数据研究增量学习的属性，主要观察结果是模拟数据在短期增量学习而不是长期增量学习中可以取得良好的性能。然后提出了一种新颖的类增量学习方法，即特征引导进化和蒸馏（FGED）。一方面，FGED鼓励模拟数据与相应的测量数据具有相同的特征关系结构，以减少它们在短期增量学习中的分布差异。另一方面，FGED采用特征蒸馏策略，同时减少先前增量类的分布差异积累，减轻长期增量学习中的灾难性遗忘。在MSTAR基准数据集和两个模拟数据集上获得的实验结果证明了FGED的有效性。