Ubiquitous UWB Ranging Error Mitigation With Application to Infrastructure-Free Cooperative Positioning

Abstract

Ultra wideband (UWB) signals are a promising choice for indoor positioning applications, since they are able to penetrate walls to a certain extent. Nevertheless, signal reflections and non-line-of-sight propagation cause bias in the measured range. This ranging error can be corrected with machine learning (ML) methods, such as convolutional neural networks (CNNs). However, these ML models often generalize poorly between different environments. In this work we present an instance-based transfer learning (TL) approach, that enables generalizing a CNN-based ranging error mitigation approach to a new situation with only a few unlabeled training samples. The performance of the UWB error correction approach is demonstrated in a real-life infrastructure-free cooperative positioning setting.