AI 中文总结
针对太赫兹近场定位中因阵列和部署带来的计算开销及传统算法在相干源下的问题，提出基于子阵列处理的分层定位框架，结合子阵列输出估计距离并利用变压器网络校正协方差，降低复杂度，提升定位精度。
AI 中文摘要
太赫兹（THz）频段近场（NF）定位在多天线系统中因波长短和波前曲率与距离相关而具有高空间分辨率。然而，减轻THz路径损耗所需的大阵列和密集部署增加了接收信号维度，给定位带来计算开销。此外，传统二维子空间算法在相干源下存在复杂度高和鲁棒性差的问题。本文提出基于子阵列（SA）处理的分层定位框架。第一步对每个SA进行一维估计以估计局部角度，第二步结合SA输出估计距离，将二维搜索简化为两个一维搜索。为处理相干源问题，基于变压器的网络预测协方差校正以优化子空间估计。仿真表明，所提分层算法将复杂度降低四个数量级。在相干场景下，基于变压器的协方差校正在5 dB信噪比时，与多重信号分类（MUSIC）相比，角度精度提高85%，距离误差减少6.5 m。
英文摘要
Terahertz (THz)-band near-field (NF) localization offers high spatial resolution due to short wavelengths and distance-dependent wavefront curvature in NF multi-antenna systems. However, large arrays and dense deployments, necessary to mitigate THz path loss, raise the received signal dimensionality, creating computational overhead for localization. Furthermore, traditional two-dimensional (2D) subspace algorithms suffer from excessive complexity and poor robustness under coherent sources. This paper proposes a hierarchical localization framework based on subarray (SA) processing. The first step performs 1D estimation per SA to estimate local angles. The second step combines SA outputs to estimate distances, reducing the 2D search to two 1D searches. To handle the drawback of coherent sources, a transformer-based network predicts a covariance correction, refining subspace estimation. Simulations show that the proposed hierarchical algorithm lowers complexity by four orders of magnitude. The transformer-based covariance correction improves angular accuracy by 85 % and reduces range error by 6.5 m at 5 dB signal-to-noise ratio in a coherent scenario compared to multiple signal classification (MUSIC).
