OFDM大规模MIMO中的近场速度估计与多普勒感知定位
Near-Field Velocity Estimation and Doppler-Aware Localization in OFDM Massive MIMO
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中文总结 AI 辅助
针对OFDM大规模MIMO近场感知的模型失配问题，提出低复杂度递归框架，联合估计径向/横向速度并实现多普勒感知定位，性能优于两种基准方法。
中文摘要 AI 辅助
在基于正交频分复用（OFDM）的大规模多输入多输出（MIMO）近场（NF）感知中，目标运动会在阵列孔径上诱导出与天线相关的双基地多普勒变化。忽略这种空间多普勒变化会导致模型失配，进而劣化近场定位性能。本文提出一种低复杂度递归框架，用于联合径向/横向速度估计与多普勒感知定位。该框架以恒定多普勒粗定位为初始化，交替执行基于闭式最小二乘估计器（LSE）的速度估计，以及与天线相关的多普勒感知定位优化。仿真与实测结果验证了所提框架相对于两种基准方法的有效性：与低复杂度恒定多普勒基线方法相比，所提算法提升了距离、角度及径向速度的估计结果，还可实现横向速度估计；在实测结果中，总定位误差从0.268m降至0.064m，径向和横向速度估计误差分别为0.032m/s和0.069m/s。与高复杂度穷举四维（4D）最大似然估计器（MLE）相比，所提方法在4D MLE采用实用有限搜索网格时，速度估计结果相当，而定位结果更准确。
英文摘要
In Orthogonal Frequency Division Multiplexing (OFDM)-based massive Multiple-Input Multiple-Output (MIMO) near-field (NF) sensing, target motion induces an antenna-dependent bistatic Doppler variation across the array aperture. Ignoring this spatial Doppler variation leads to a model mismatch that degrades NF localization. In this paper, we propose a low-complexity recursive framework for joint radial/transverse velocity estimation and Doppler-aware localization. Initialized by a constant-Doppler coarse localization, the method alternates between closed-form Least Squares Estimator (LSE)-based velocity estimation and antenna-dependent Doppler-aware localization refinement. Simulation and measurement results demonstrate the effectiveness of the proposed framework against two benchmark methods. Compared with a low-complexity constant-Doppler baseline method, the proposed algorithm improves range, angle, and radial velocity estimation results, while also enabling transverse velocity estimation. In the measurement results, the overall localization error decreases from 0.268 m to 0.064 m. The radial and transverse velocity estimation errors are 0.032 m/s and 0.069 m/s, respectively. Compared with a high-complexity exhaustive four-dimensional (4D) Maximum Likelihood Estimator (MLE), the proposed method achieves comparable velocity estimation results while yielding a more accurate localization result when the 4D MLE has a practical finite search grid.
