Positioning and sensing over wireless networks are imperative for many emerging applications. However, traditional wireless channel models cannot be used for sensing the attitude of the user equipment (UE), since they over-simplify the UE as a point target. In this paper, a comprehensive electromagnetic propagation modeling (EPM) based on electromagnetic theory is developed to precisely model the near-field channel. For the noise-free case, the EPM model establishes the non-linear functional dependence of observed signals on both the position and attitude of the UE. To address the difficulty in the non-linear coupling, we first propose to divide the distance domain into three regions, separated by the defined Phase ambiguity distance and Spacing constraint distance. Then, for each region, we obtain the closed-form solutions for joint position and attitude estimation with low complexity. Next, to investigate the impact of random noise on the joint estimation performance, the Ziv-Zakai bound (ZZB) is derived to yield useful insights. The expected Cram\'er-Rao bound (ECRB) is further provided to obtain the simplified closed-form expressions for the performance lower bounds. Our numerical results demonstrate that the derived ZZB can provide accurate predictions of the performance of estimators in all signal-to-noise ratio (SNR) regimes. More importantly, we achieve the millimeter-level accuracy in position estimation and attain the 0.1-level accuracy in attitude estimation.

翻译：针对新兴应用场景，无线网络定位与感知技术至关重要。然而传统无线信道模型将用户设备简化为点目标，无法实现姿态感知。本文基于电磁理论发展出完整的电磁传播建模方法，对近场信道进行精确建模。在无噪声情况下，该模型建立了观测信号与用户设备位置及姿态之间的非线性函数依赖关系。为克服非线性耦合难题，我们首先将距离域划分为三个区域，由定义的相位模糊距离和间距约束距离分隔。随后针对每个区域，提出低复杂度的联合位置与姿态估计闭式解。为探究随机噪声对联合估计性能的影响，推导了Ziv-Zakai界以获取关键见解，并进一步提出期望克拉美-罗界以得到性能下界的简化闭式表达式。数值结果表明，所推导的ZZB能在所有信噪比范围内准确预测估计器性能。更重要的是，我们的方法在位置估计中达到毫米级精度，姿态估计达到0.1度级精度。