Positioning and sensing over wireless networks are imperative for many emerging applications. However, since traditional wireless channel models over-simplify the user equipment (UE) as a point target, they cannot be used for sensing the attitude of the UE, which is typically described by the spatial orientation. 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：无线网络上的定位与感知对许多新兴应用至关重要。然而，由于传统无线信道模型将用户设备（UE）过度简化为点目标，因此无法用于感知通常由空间方向描述的UE姿态。本文基于电磁理论发展了全面的电磁传播建模（EPM），以精确建模近场信道。对于无噪声情况，EPM模型建立了观测信号对UE位置和姿态的非线性函数依赖关系。为解决非线性耦合的难题，我们首先提出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。然后，针对每个区域，我们获得了联合位置与姿态估计的低复杂度闭式解。接着，为探究随机噪声对联合估计性能的影响，推导了Ziv-Zakai界（ZZB）以提供有益见解。进一步给出了期望的克拉美-罗界（ECRB），以获得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（SNR）区间准确预测估计器的性能。更重要的是，我们实现了毫米级的位置估计精度以及0.1级的姿态估计精度。