三维无线电断层成像的正则化方法基准测试
Benchmarking Regularization Methods For 3D Radio Tomographic Imaging
AI总结：
本文在x3DPRA框架下系统比较Ridge、TV和Tensor Nuclear三种正则化方法，发现TV重建质量最佳但速度非最快，为6G ISAC高分辨率三维RTI系统提供指导。
AI中文摘要：
集成感知与通信（ISAC）是6G的一项重要技术，使无线系统能够感知其物理环境。无线电断层成像（RTI）是ISAC中一种潜在的无设备感知技术，它从接收信号强度（RSS）测量中重建物体位置和形状。它可以通过提供具有波长分辨率的窄带模态来补充雷达和激光雷达等其他技术。最近，RTI的一种扩展版本，称为扩展无相位Rytov近似（x3DPRA），已被开发出来以提高RTI重建质量、估计材料参数，并将RTI从二维（2D）扩展到三维（3D）。然而，三维公式比二维形式更加不适定，因为测量数量远少于未知体素的数量。在这项工作中，我们在x3DPRA框架内系统地评估和比较了三种不同的正则化方法（Ridge、Total Variation和Tensor Nuclear）。我们通过评估三种方法的重建质量和计算运行时间，提供了详细的性能分析。我们的研究结果表明，Total Variation正则化提供了最佳的重建质量，但并非最快的运行时间，这为未来ISAC应用中开发高分辨率三维RTI系统提供了指导。
英文摘要：
Integrated Sensing and Communication (ISAC) is an important technology for 6G, enabling wireless systems to perceive their physical environment. Radio Tomographic Imaging (RTI) is a potential technique for device-free sensing in ISAC, reconstructing object locations and shapes from Received Signal Strength (RSS) measurements. It can complement other techniques, such as radar and LiDAR, by providing a narrowband modality with wavelength resolution. Recently, an extended version of RTI, known as the extended phaseless Rytov approximation (x3DPRA), has been developed to enhance RTI reconstruction quality, estimate material parameters, and extend RTI from two to three dimensions (3D). However, the 3D formulation is even more ill-posed than the 2D form, as the number of measurements is far fewer than the number of unknown voxels. In this work, we systematically evaluate and compare three distinct regularization approaches (Ridge, Total Variation, and Tensor Nuclear) within the x3DPRA framework. We provide a detailed performance analysis by evaluating both reconstruction quality and computational runtime across the three methods. Our findings show that Total Variation regularization provides the best reconstruction quality but not the fastest runtime, providing guidance for developing high-resolution 3D RTI systems for future ISAC applications.
