Wireless imaging has become a vital function in future integrated sensing and communication (ISAC) systems. However, traditional model-based and data-driven deep learning imaging methods face challenges related to multipath extraction, dataset acquisition, and multi-scenario adaptation. To overcome these limitations, this study innovatively combines implicit neural representation (INR) with explicit physical models to realize wireless imaging in reconfigurable intelligent surface (RIS)-aided ISAC systems. INR employs neural networks (NNs) to project physical locations to voxel values, which is indirectly supervised by measurements of channel state information with physics-informed loss functions. The continuous shape and scattering characteristics of targets are embedded into NN parameters through training, enabling arbitrary image resolutions and off-grid voxel value prediction. Additionally, three issues related to INR-based imager are further addressed. First, INR is generalized to enable efficient imaging under multipath interference by jointly learning image and multipath information. Second, the imaging speed and accuracy for dynamic targets are enhanced by embedding prior image information. Third, imaging results are employed to assist in RIS phase design for improved communication performance. Extensive simulations demonstrate that the proposed INR-based imager significantly outperforms traditional model-based methods with super-resolution abilities, and the focal length characteristics of the imaging system is revealed. Moreover, communication performance can benefit from the imaging results. Part of the source code for this paper can be accessed at https://github.com/kiwi1944/INRImager

翻译：无线成像已成为未来集成感知与通信（ISAC）系统的关键功能。然而，基于模型的传统成像方法与数据驱动的深度学习成像方法在路径多径提取、数据集获取及多场景适应性方面面临挑战。为突破这些限制，本研究创新性地将隐式神经表征（INR）与显式物理模型相结合，实现了可重构智能表面（RIS）辅助ISAC系统中的无线成像。INR利用神经网络（NNs）将物理位置映射至体素值，并通过物理信息损失函数对信道状态信息测量值进行间接监督。目标的连续形状与散射特性通过训练嵌入神经网络参数，从而实现任意图像分辨率与离网格体素值预测。此外，本文进一步解决了基于INR成像器的三个关键问题：首先，通过联合学习图像与多径信息，将INR推广至多径干扰下的高效成像场景；其次，通过嵌入先验图像信息提升动态目标的成像速度与精度；第三，利用成像结果辅助RIS相位设计以提升通信性能。大量仿真实验表明，所提出的基于INR的成像器在超分辨率能力方面显著优于传统基于模型的方法，并揭示了成像系统的焦距特性。此外，通信性能可受益于成像结果。本文部分源代码可通过 https://github.com/kiwi1944/INRImager 获取。