非视线传播
计算机科学
测距
定位系统
多向性
卡尔曼滤波器
实时计算
频道(广播)
混合定位系统
室内定位系统
人工智能
无线
超宽带
电信
工程类
节点(物理)
结构工程
加速度计
操作系统
作者
Dae-Ho Kim,Arshad Farhad,Jae-Young Pyun
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-01-15
卷期号:10 (2): 1822-1835
被引量:15
标识
DOI:10.1109/jiot.2022.3209735
摘要
It is known that an ultrawideband (UWB)-based indoor positioning system (IPS) has superior positioning performance and can meet the requirements of location-based services (LBSs) as the Internet of Things (IoT) applications. However, there is a limitation of UWB positioning when it is conducted at the nonline-of-sight (NLOS) channels degrading the UWB ranging accuracy at indoor environments. In this article, we propose an artificial intelligence (AI) applied UWB positioning system that can enhance the positioning performance by classifying channel conditions with channel impulse response (CIR) of the received UWB signal. The proposed system mitigates the positioning degradation caused by the NLOS situations by performing extended Kalman filter (EKF) localization and long short-term memory (LSTM) training of the observed channel status. The main feature of the proposed UWB positioning method is that it can be used even at unknown locations not trained with the LSTM model learning the channel status, because of our training strategy of not the position coordinates, but the UWB ranging error between UWB devices corresponding to CIR of the received UWB signal. This article provides the experimental setup and performance evaluation results of the proposed system. The evaluation results showed that the proposed AI-applied UWB positioning method significantly improved its accuracy performance compared with the existing positioning methods.
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