AI-Enhanced Distance Estimation via Radio Chip Link Quality Metrics and Time-of-Flight Analysis With UWB Technology: A Comparative Evaluation

被引：0

作者：

Taktak, Maissa ^{[1
,2
]}

Baazaoui, Mohamed Khalil ^{[1
,2
]}

Ketata, Ilef ^{[1
,2
]}

Sahnoun, Salwa ^{[2
]}

Fakhfakh, Ahmed ^{[2
]}

Derbel, Faouzi ^{[1
]}

机构：

[1] Leipzig Univ Appl Sci, Smart Diagnost & Online Monitoring, D-04107 Leipzig, Germany

[2] Networks, Digital Res Ctr Sfax CRNS, Lab Signals Syst Artificial Intelligence SM RTS, Sfax 3018, Tunisia

来源：

IEEE SENSORS LETTERS | 2024年 / 8卷 / 10期

关键词：

Sensor applications; distance estimation; link quality metrics (LQM); machine learning (ML); Time-of-Flight (ToF); ultra-wideband (UWB); wireless sensor network (WSN);

D O I：

10.1109/LSENS.2024.3462600

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Precise distance estimation is essential in various fields, influencing customary aspects from daily activities to advanced research. In wireless sensor networks (WSN) accurate distance estimation is crucial for different applications, such as localization, energy efficiency, dynamic routing, and coverage optimization. In this letter, we strive to assess distance accurate estimation across various technologies, including a sub-GHz low-power, low-data-rate radio chip, and the ultra-wideband (UWB) transceiver. We utilize a combination of Time-of-Flight (ToF), link quality metrics (LQM), and machine learning (ML) techniques to elucidate the strengths and limitations of each technology.

引用

页数：4