存在阵列误差的5G NR单基地定位：数据与模型驱动框架及实验结果
5G NR monostatic positioning with array impairments: Data-and-model-driven framework and experiment results
AI总结：
针对5G NR单基地室内定位中高维信息复杂、不规则阵列误差影响精度的问题，提出融合transformer与MUSIC区域决策机制的数据模型驱动框架，可校准阵列误差、提升定位精度。
摘要：
本文提出了一种适用于单基地配置下5G新空口（NR）室内定位的智能框架，核心目标是同时估计到达角与到达时间，这需要从接收信号的天线维度和子载波维度中提取相关信息。为解决高维信息矩阵的复杂性以及不规则阵列误差带来的挑战，我们设计了一种深度学习方案。我们认识到任意两个子载波与天线之间的相位差编码了目标的空间信息，因此认为相较于擅长局部特征提取的卷积神经网络，transformer网络更适合解决该问题。为进一步提升网络的拟合能力，我们将transformer与基于模型的多信号分类（MUSIC）区域决策机制相结合。数值仿真与现场测试结果表明，所提框架可有效校准与角度相关的不规则阵列误差，提升定位精度。
英文摘要：
In this article, we present an intelligent framework for 5G new radio (NR) indoor positioning under a monostatic configuration. The primary objective is to estimate both the angle of arrival and time of arrival simultaneously. This requires capturing the pertinent information from both the antenna and subcarrier dimensions of the receive signals. To tackle the challenges posed by the intricacy of the high-dimensional information matrix, coupled with the impact of irregular array errors, we design a deep learning scheme. Recognizing that the phase difference between any two subcarriers and antennas encodes spatial information of the target, we contend that the transformer network is better suited for this problem compared to the convolutional neural network which excels in local feature extraction. To further enhance the network's fitting capability, we integrate the transformer with a model-based multiple-signal-classification (MUSIC) region decision mechanism. Numerical results and field tests demonstrate the effectiveness of the proposed framework in accurately calibrating the irregular angle-dependent array error and improving positioning accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
