  The electromagnetic inverse problem has long been a research hotspot. This
study aims to reverse radar view angles in synthetic aperture radar (SAR)
images given a target model. Nonetheless, the scarcity of SAR data, combined
with the intricate background interference and imaging mechanisms, limit the
applications of existing learning-based approaches. To address these
challenges, we propose an interactive deep reinforcement learning (DRL)
framework, where an electromagnetic simulator named differentiable SAR render
(DSR) is embedded to facilitate the interaction between the agent and the
environment, simulating a human-like process of angle prediction. Specifically,
DSR generates SAR images at arbitrary view angles in real-time. And the
differences in sequential and semantic aspects between the view
angle-corresponding images are leveraged to construct the state space in DRL,
which effectively suppress the complex background interference, enhance the
sensitivity to temporal variations, and improve the capability to capture
fine-grained information. Additionally, in order to maintain the stability and
convergence of our method, a series of reward mechanisms, such as memory
difference, smoothing and boundary penalty, are utilized to form the final
reward function. Extensive experiments performed on both simulated and real
datasets demonstrate the effectiveness and robustness of our proposed method.
When utilized in the cross-domain area, the proposed method greatly mitigates
inconsistency between simulated and real domains, outperforming reference
methods significantly.
