Real-time remote measurement of distance using ultra-wideband (UWB) sensors

Abstract

Distance measurement is significant for ensuring the safety and serviceability of engineering structures. Recently, ultra-wideband (UWB) sensors have offered an alternative real-time remote sensing solution to measure distances. UWB sensors exhibit small size, low cost, low energy consumption, and high robustness to weather conditions, but their ranging accuracy is still limited. This paper presents two machine learning approaches to achieve high accuracy and high frequency, simultaneously. The first approach integrates a convolutional neural network, a long short-term memory module, and a regression module. The second approach integrates two random forest models. These two approaches were implemented into measurement from UWB sensors deployed on a highway bridge and outperformed the state-of-the-art approaches in terms of measurement accuracy and output frequency. The configurations and key parameters of the two approaches were evaluated and improved. This research enhances the capability of measuring distances and deformations for structural health monitoring.