EMWaveNet: Physically Explainable Neural Network Based on Electromagnetic Wave Propagation for SAR Target Recognition

Deep learning technologies have significantly improved performance in the field of synthetic aperture radar (SAR) image target recognition compared with traditional methods. However, the inherent lack of interpretability in deep learning models results in low transparency during the decision-making process, which hinders their widespread application in practice. This is especially true in SAR applications, where the credibility and reliability of model predictions are crucial. The complexity and insufficient explainability of deep networks have become a bottleneck for their application. To tackle this issue, this study proposes a physically explainable framework for complex-valued SAR image recognition, designed based on the physical process of microwave propagation. This framework uses complex-valued SAR data to explore the amplitude and phase information and its intrinsic physical properties. The network architecture is fully parameterized, with all learnable parameters endowed with clear physical meanings. Experiments on both the complex-valued MSTAR dataset and a self-built Qilu-1 complex-valued dataset were conducted to validate the effectiveness of framework. The deoverlapping capability of EMWaveNet enables accurate recognition of overlapping target categories, whereas other models are nearly incapable of performing such recognition. Against 0-dB forest background noise, it boasts a 20% accuracy improvement over traditional neural networks. When targets are 60% occluded by noise, it still outperforms other models by 9%. An end-to-end complex-valued SAR automatic target recognition (SAR-ATR) algorithm is constructed to perform recognition tasks in interference SAR scenarios. The results demonstrate that the proposed method possesses a strong physical decision logic, high physical explainability, and robustness, as well as excellent dealiasing capabilities. Finally, a perspective on future applications is provided.