NLOS Identification and Mitigation Using Low-Cost UWB Devices

Abstract

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.

LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting

“…Recent feature-based approaches provided encouraging results on the NLOS identification and mitigation by properly analyzing the characteristics of the received UWB signal [19,22]. Machine learning approaches proved to be well suited for identifying NLOS measurements as well, while they currently do not seem to provide significant improvements for NLOS effects mitigation [23,24].…”

“…Recent feature-based approaches provided encouraging results on the NLOS identification and mitigation by properly analyzing the characteristics of the received UWB signal [19,22]. Machine learning approaches proved to be well suited for identifying NLOS measurements as well, while they currently do not seem to provide significant improvements for NLOS effects mitigation [23,24].The integration with the information provided by other sensors can be considered to mitigate the UWB measurement error and regularize the estimated trajectory, e.g., pedestrian dead reckoning based on the inertial sensor measurements [25]. Furthermore, a cooperative positioning approach can also be considered when the position of multiple devices has to be simultaneously estimated [26].…”

An Efficient NLOS Errors Mitigation Algorithm for TOA-Based Localization

“…However, false-alarm and mis-detection cannot be avoided in this process. Besides, when there are fewer than three remaining nodes for 2D TOA positioning (or four for 3D TOA positioning), the source cannot be localized, but NLOS error mitigation methods are not restricted by the number of LOS nodes [19][20][21][22][23][24][25][26].…”

“…Besides, in some rare cases, the computation would fail because one of the inequality constraints is not satisfied. Two convex relaxation methods, i.e., the semidefinite relaxation (SDR) and the second-order cone relaxation (SOCR) methods, were developed in [22]. These methods assume a known upper bound about the NLOS errors.…”

“…In [23], Gao et al proposed a robust least squares (RLS) method to jointly estimate the source position and the transmission time when address the problem of NLOS error mitigation in the asynchronous sensor network.…”