Millimeter Wave Wireless Assisted Robot Navigation With Link State Classification
Millimeter Wave Wireless Assisted Robot Navigation With Link State Classification
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DOI：
10.1109/ojcoms.2022.3155572
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发表时间：
2021-10
影响因子：
7.9
通讯作者：
Mingsheng Yin;A. Veldanda;Amee Trivedi;Jeff Zhang;K. Pfeiffer;Yaqi Hu;S. Garg;E. Erkip;L. Righetti;S. Rangan
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文献类型：
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作者：
Mingsheng Yin;A. Veldanda;Amee Trivedi;Jeff Zhang;K. Pfeiffer;Yaqi Hu;S. Garg;E. Erkip;L. Righetti;S. Rangan
由于能够捕获高角度和时间分辨率测量值，毫米波（MMWAVE）带对高精度定位应用引起了极大的关注。本文探讨了基于MMWave的目标定位问题的定位，固定目标广播MMWave信号和移动机器人代理商试图捕获信号以定位并导航到目标。提出了一个三阶段的过程：首先，移动代理使用张量分解方法检测多径通道组件并估算其参数。其次，然后使用机器学习训练的分类器来预测链路状态，这意味着最强的路径是视线（LOS）或非LOS（NLOS）。对于NLOS案例，链路状态预测指标还确定最强的路径是否通过一种或多种反射到达。第三，基于链接状态，代理要么遵循估计的角度，要么使用计算机视觉或其他传感器来探索和绘制环境。该方法在补充射线跟踪的室内环境的大数据集中进行了证明，以模拟无线传播。路径估计和链接状态分类还集成到最新的神经同时定位和映射（SLAM）模块中，以增强相机和基于激光雷达的导航。结果表明，链接状态分类器可以成功概括到训练集之外的全新环境。此外，具有无线路径估计和链接状态分类器的神经 - 链接模块为目标提供了快速导航，靠近知道目标位置的基线。
The millimeter wave (mmWave) bands have attracted considerable attention for high precision localization applications due to the ability to capture high angular and temporal resolution measurements. This paper explores mmWave-based positioning for a target localization problem where a fixed target broadcasts mmWave signals and a mobile robotic agent attempts to capture the signals to locate and navigate to the target. A three-stage procedure is proposed: First, the mobile agent uses tensor decomposition methods to detect the multipath channel components and estimate their parameters. Second, a machine-learning trained classifier is then used to predict the link state, meaning if the strongest path is line-of-sight (LOS) or non-LOS (NLOS). For the NLOS case, the link state predictor also determines if the strongest path arrived via one or more reflections. Third, based on the link state, the agent either follows the estimated angles or uses computer vision or other sensor to explore and map the environment. The method is demonstrated on a large dataset of indoor environments supplemented with ray tracing to simulate the wireless propagation. The path estimation and link state classification are also integrated into a state-of-the-art neural simultaneous localization and mapping (SLAM) module to augment camera and LIDAR-based navigation. It is shown that the link state classifier can successfully generalize to completely new environments outside the training set. In addition, the neural-SLAM module with the wireless path estimation and link state classifier provides rapid navigation to the target, close to a baseline that knows the target location.