Deep Gated Recurrent Unit-Based 3D Localization for UWB Systems

IEEE Access

Xin Kang¹  Doan Tan Anh Nguyen²  Jingon Joung² 

[1] Center for Intelligent Networking and Communications (CINC), University of Electronic Science and Technology of China (UESTC), Chengdu, China;School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, South Korea;

关键词: 3D localization;    deep learning;    gated recurrent unit (GRU);    recurrent neural network (RNN);    ultra-wideband (UWB) system;   

DOI  :  10.1109/ACCESS.2021.3077906

来源: DOAJ

【 摘 要 】

The localization system has been extensively studied because of its diverse applicability, for example, in the Internet of Things, automatic management, and unmanned aerial vehicle services. There have been numerous studies on localization in two-dimensional (2D) environments, but those in three-dimensional (3D) environments are scarce. In this paper, we propose a novel localization method that utilizes the gated recurrent unit (GRU) and ultra-wideband (UWB) signals. For the purpose of this study, we considered that the UWB transmitter (Tx) and many UWB receivers (Rx) were placed inside a confined space. The input of the proposed model was generated from the UWB signals that are sent from the Tx to the Rxs, and the output was the location of the Tx. The proposed GRU-based model converts the localization problem into a regression problem by combining the ranging and positioning phase. Thus, the proposed model can directly estimate the location of the Tx. Our proposed GRU-based method achieves 15 and four times shorter execution times for the training and testing, respectively, compared to the existing convolutional neural network (CNN)-based localization methods. The input data can also be easily generated with low complexity. The rows of the input matrix are the downsampled version of the UWB received signal. Throughout numerous simulation results, our novel localization method can achieve a lower root-mean-squared error up to 0.8 meters compared to the recently proposed existing CNN-based method. Furthermore, the proposed method operates well inside a confined space with fixed volume but varying width, height, and depth.

【 授权许可】

Unknown