利用蜂窝多径分量时延与角度信息的扩展FastSLAM
Extended FastSLAM Using Cellular Multipath Component Delays and Angular Information
AI总结：
本文提出利用LTE下行信号的多径时延与到达角，通过扩展FastSLAM联合估计车辆、基站和虚拟发射机位置，在530米行驶距离内将水平定位误差从15米降至6米以内。
摘要：
在其它导航技术面临挑战的场景中，利用蜂窝信号的机会性导航具有吸引力。本文中，来自两个邻近商用基站（BS）的长期演进（LTE）下行链路信号由安装在乘用车上的大规模天线阵列接收。从接收信号中提取的多径分量（MPC）时延和到达角（AOA）被用于通过扩展快速同时定位与建图（FastSLAM）算法联合估计车辆、发射机以及虚拟发射机（VT）的位置。结果表明，该算法能够准确估计车辆和发射机（以及虚拟发射机）的位置。在行驶530米后，与本体感知融合的SLAM车辆水平位置误差小于6米，而无辅助的本体感知导致水平误差为15米。
英文摘要：
Opportunistic navigation using cellular signals is appealing for scenarios where other navigation technologies face challenges. In this paper, long-term evolution (LTE) downlink signals from two neighboring commercial base stations (BS) are received by a massive antenna array mounted on a passenger vehicle. Multipath component (MPC) delays and angle-of-arrival (AOA) extracted from the received signals are used to jointly estimate the positions of the vehicle, transmitters, and virtual transmitters (VT) with an extended fast simultaneous localization and mapping (FastSLAM) algorithm. The results show that the algorithm can accurately estimate the positions of the vehicle and the transmitters (and virtual transmitters). The vehicle's horizontal position error of SLAM fused with proprioception is less than 6 meters after a traversed distance of 530 meters, whereas un-aided proprioception results in a horizontal error of 15 meters.
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。
