标题： 在阵列校准不完善下的近场联合运动、角度和范围估计 显示英文标题

标题： Joint Motion, Angle, and Range Estimation in Near-Field under Array Calibration Imperfections

摘要： 超大质量多输入多输出MIMO（UM-MIMO）在高频下利用大型天线阵列，将通信范式转变为辐射近场（NF），其中球面波前使得目标位置和速度的全矢量估计成为可能。 然而，在这种情况下，位置和运动参数本质上是耦合的，使其联合估计计算量很大。 为克服这一问题，我们提出了一种新方法，使用二维离散傅里叶变换（2D-DFT）将接收到的二维时空信号投影到角度-多普勒域。 我们的分析表明，得到的角度扩展以目标的真实角度为中心，其宽度由目标的距离决定。同样，横向运动会引起以真实径向速度为中心的多普勒扩展，多普勒扩展的宽度与横向速度成正比。 利用这些频谱特性，我们开发了一种低复杂度算法，提供角度、距离和速度的粗略估计，随后通过独立应用于每个参数的一维多重信号分类（MUSIC）进行细化。 所提出的方法实现了对NF目标运动参数的准确和高效估计。 仿真结果表明，与最大似然估计相比，位置和速度估计的归一化均方误差（NMSE）为-40 dB，同时显著降低了计算复杂度。 显示英文摘要

摘要： Ultra-massive multiple-input multiple-output MIMO (UM-MIMO) leverages large antenna arrays at high frequencies, transitioning communication paradigm into the radiative near-field (NF), where spherical wavefronts enable full-vector estimation of both target location and velocity. However, location and motion parameters become inherently coupled in this regime, making their joint estimation computationally demanding. To overcome this, we propose a novel approach that projects the received two-dimensional space-time signal onto the angle-Doppler domain using a two-dimensional discrete Fourier transform (2D-DFT). Our analysis reveals that the resulting angular spread is centered at the target's true angle, with its width determined by the target's range. Similarly, transverse motion induces a Doppler spread centered at the true radial velocity, with the width of Doppler spread proportional to the transverse velocity. Exploiting these spectral characteristics, we develop a low-complexity algorithm that provides coarse estimates of angle, range, and velocity, which are subsequently refined using one-dimensional multiple signal classification (MUSIC) applied independently to each parameter. The proposed method enables accurate and efficient estimation of NF target motion parameters. Simulation results demonstrate a normalized mean squared error (NMSE) of -40 dB for location and velocity estimates compared to maximum likelihood estimation, while significantly reducing computational complexity.