NLOS Identification and Mitigation Using Low-Cost UWB Devices

Indoor location systems based on ultra-wideband (UWB) technology have become very popular in recent years following the introduction of a number of low-cost devices on the market capable of providing accurate distance measurements. Although promising, UWB devices also suffer from the classic problems found when working in indoor scenarios, especially when there is no a clear line-of-sight (LOS) between the emitter and the receiver, causing the estimation error to increase up to several meters. In this work, machine learning (ML) techniques are employed to analyze several sets of real UWB measurements, captured in different scenarios, to try to identify the measurements facing non-line-of-sight (NLOS) propagation condition. Additionally, an ulterior process is carried out to mitigate the deviation of these measurements from the actual distance value between the devices. The results show that ML techniques are suitable to identify NLOS propagation conditions and also to mitigate the error of the estimates when there is LOS between the emitter and the receiver.

近年来，基于超宽带（UWB）技术的室内定位系统随着市场上低成本设备的引入而日益受到青睐，这些设备能够提供精确的距离测量。尽管前景广阔，UWB设备在室内场景中工作亦不可避免地遭遇了经典问题，尤其是在发射端与接收端之间缺乏清晰视线的条件下，导致估计误差增加至数米。本研究中，我们采用了机器学习（ML）技术来分析不同场景下捕获的多组真实UWB测量数据，旨在识别面临非视距（NLOS）传播条件的测量。此外，还进行了一项后续处理，以减轻这些测量值与设备实际距离之间的偏差。结果表明，机器学习技术适用于识别NLOS传播条件，并在发射端与接收端之间存在视线（LOS）时，也有助于减轻估计误差。

搜集汇总

背景与挑战

背景概述

该数据集提供了低成本UWB设备在不同传播条件下的测量数据，旨在通过机器学习技术识别NLOS条件并缓解测量误差。数据集包含LOS、NLOS Hard和NLOS Soft三种场景的MATLAB格式数据，适用于室内定位系统的研究和开发。