Machine Learning and Information Fusion Group, Precise Positioning and Analytics Department, Fraunhofer Institute for Integrated Circuits IIS, Nordostpark 84, 90411 Nürnberg, Germany.
Machine Learning and Data Analytics Lab, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Carl-Thiersch-Straße 2b, 91052 Erlangen, Germany.
Sensors (Basel). 2019 Mar 2;19(5):1064. doi: 10.3390/s19051064.
Radio-based locating systems allow for a robust and continuous tracking in industrial environments and are a key enabler for the digitalization of processes in many areas such as production, manufacturing, and warehouse management. Time difference of arrival (TDoA) systems estimate the time-of-flight (ToF) of radio burst signals with a set of synchronized antennas from which they trilaterate accurate position estimates of mobile tags. However, in industrial environments where multipath propagation is predominant it is difficult to extract the correct ToF of the signal. This article shows how deep learning (DL) can be used to estimate the position of mobile objects directly from the raw channel impulse responses (CIR) extracted at the receivers. Our experiments show that our DL-based position estimation not only works well under harsh multipath propagation but also outperforms state-of-the-art approaches in line-of-sight situations.
基于无线电的定位系统可在工业环境中实现强大且连续的跟踪，是许多领域（如生产、制造和仓库管理）流程数字化的关键推动因素。到达时间差 (TDoA) 系统使用一组同步天线估算无线电突发信号的飞行时间 (ToF)，并从这些天线进行三边测量，以获得移动标签的精确位置估计。然而，在多径传播占主导地位的工业环境中，很难提取信号的正确 ToF。本文展示了如何使用深度学习 (DL) 直接从接收器提取的原始信道冲激响应 (CIR) 估算移动目标的位置。我们的实验表明，我们基于 DL 的位置估计不仅在恶劣的多径传播条件下表现良好，而且在视距情况下也优于最先进的方法。
