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 listen to the signals to locate and navigate to the target. A three strage procedure is proposed: First, the mobile agent uses tensor decomposition methods to detect the wireless paths and their angles. 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 explores 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-ofthe-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.
翻译:毫米波(mmWave)波段吸引了对高精密本地化应用的极大关注,这是因为能够捕捉高角和时间分辨率测量。本文探讨了在固定目标广播 毫米Wave 信号和移动机器人代理器试图监听信号以定位和导航到目标的情况下,基于目标定位和导航的定位问题的目标本地化问题的基于毫米Wave定位定位定位定位定位定位定位。建议了三种字符串程序:首先,移动剂使用强光分解方法来探测无线路径及其角度。第二,然后使用机器学习的训练有素的分类器来预测链接状态,这意味着如果最强的路径是直线(LOS)或非近线分辨率(NLOS) 。对于 NLOS 来说,链接状态预测器还可以确定最强的路径是否通过一个或多个反映射信号。第三,根据链接状态,该代理器要么遵循估计的角度或探索环境。该方法在大型室内环境数据集上展示,辅之以光线追踪,以模拟无线传播。路径估计和连接状态分类还被整合成一个基于状态的内线-近线线线线线(LOS-直径定位) 具体目标路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路至显示到全全全全全级路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路与L。,该。,该,该,并显示,该,加加加升级,显示全全全全全全全,加加升级,再升级,再升级,再升级,再升级,再升级,再升级,再升级,再升级,直图再升级,再升级,再升级,