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

Abstract

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.