篇名：Mobile Positioning Based on TAE-GRU
作者：Canyang Guo(郭灿阳), Ling Wu*, Cheng Shi, Chi-Hua Chen(陈志华)
来源：Proceedings of the 2021 Web Conference (formerly known as WWW conference)(TheWebConf 2021)
年份：2021
DOI: 10.1145/3442442.3451146
文章摘要：
This paper motivates to solve the multiple mapping of Received Signal Strength Indications (RSSIs) and location estimating problem in mobile positioning. A mobile positioning method based on Time-distributed Auto Encoder and Gated Recurrent Unit (TAE-GRU) is proposed to realize the mobile positioning. To distinguish the identical RSSI of different temporal steps, this paper develops a reconstructed model based on Time-distributed Auto Encoder (TAE), which is conducive for further learning of the estimated model. Among them, time-distributed technology is utilized to translate the data of each temporal step separately accommodating the temporal characteristics of RSSI data. Besides, an estimated model based on Gated Recurrent Unit (GRU) is developed to learn the temporal relationship of RSSI data to estimate the locations of mobile devices. Combining the TAE model and GRU model, the proposed model is provided with the capability of solving multiple mapping and mobile positioning dilemma. Massive experimental results demonstrated that the proposed method provides superior performance than comparative methods when solving multiple mapping and positioning problems.
本文旨在解决移动定位中接收信号强度指示的多重映射和位置估计问题。提出了一种基于时间分布自动编码器和选通递归单元（TAE-GRU）的移动定位方法来实现移动定位。为了区分不同时间步长下相同的RSSI，本文提出了一种基于时间分布自动编码器（TAE）的重构模型，这有助于进一步学习估计模型。其中，利用时间分布技术，根据RSSI数据的时间特性，分别对每个时间步长的数据进行转换。此外，本文还提出了一种基于选通递归单元（GRU）的估计模型，用于学习RSSI数据的时间关系来估计移动设备的位置。将TAE模型与GRU模型相结合，使该模型具有解决多重映射和移动定位难题的能力。大量的实验结果表明，该方法在解决多个映射和定位问题时比比较方法具有更好的性能。
1. The structure of TAE
2. The structure of GRU