AI 中文总结
该研究提出基于无线通信频率衍射机制的环境物体感知框架，开发参数化衍射信道模型，推导ML估计方法及通过CRB量化性能极限，整合多种波传播近似，推导菲涅耳数缩放定律，数值结果显示ML估计器接近CRB，突出衍射建模在遮挡表征中的作用。
AI 中文摘要
本文提出了一个严格的框架，用于利用无线通信频率下普遍存在的衍射机制来感知环境物体。具体而言，我们开发了一个物理上一致的参数化衍射信道模型，推导了用于估计遮挡形状、范围和源到达方向（DoA）的最大似然（ML）方法，并通过克拉美 - 罗界（CRB）量化基本性能极限。在基于物理的建模中，我们整合了波传播的各种近似（远场、傍轴菲涅耳和精确近场区域），实现了广泛的适用性。基础模型与频率无关，我们推导了菲涅耳数缩放定律，该定律可跨载波频率、物体大小和范围映射衍射图案，进而映射估计问题。我们量化了最大似然估计性能及其与CRB的关系，并研究了本文中开发的建模近似的影响。数值结果表明，在中等到高信噪比（SNR）下，ML估计器紧密接近CRB，并突出了基于衍射的建模在高保真遮挡表征中的效用。
英文摘要
This paper proposes a rigorous framework for sensing of environmental objects using diffraction mechanisms prevalent at wireless communication frequencies. Specifically, we develop a physics-consistent parameterized diffraction channel model, derive maximum likelihood (ML) approaches for estimating the blockage shape, range, and source directions of arrival (DoAs), and quantify fundamental performance limits via the Cramér--Rao bound (CRB). In our physics-based modeling, we integrate various approximations for the wave propagation (far-field, paraxial Fresnel, and exact near-field regimes), enabling a wide range of applicability. The underlying model is frequency-agnostic, and we derive Fresnel-number scaling laws that map the diffraction pattern, and hence the estimation problem, across carrier frequency, object size, and range. We quantify the maximum likelihood estimation performance and its relationship to the CRB, and we study the impact of the modeling approximations developed in this work. Numerical results demonstrate that ML estimators closely approach the CRB at moderate to high signal-to-noise ratio (SNR), and highlight the utility of diffraction-based modeling for high-fidelity blockage characterization.
