Transfer learning-based UWB indoor localization using MHT-MDC and clusterization-based sparse fingerprinting

Abstract

Indoor localization systems have become more and more popular. Several technologies are intensively studied with application to high precision object localization in such environments. Ultra-wideband (UWB) is one of the most promising, as it combines relatively low cost and high localization accuracy, especially compared to Beacon or WiFi. Nevertheless, we noticed that the leading UWB systems’ accuracy is far below the values declared in the documentation. To achieve high localization accuracy, low fingerprinting complexity, and tolerance to significant random errors, we propose a Multiple Hypothesis Tracker with Direction Motion Constraint (MHT-MDC) algorithm followed by a transfer learning approach. We perform fingerprinting using a dense grid in a controlled environment to train the neural network. Thanks to transfer learning, full fingerprinting is not necessary to obtain high localization accuracy when the UWB system is deployed in a new localization. We demonstrate that thanks to transfer learning, high localization accuracy can be maintained when only 7% of fingerprinting samples from a new localization are used to update the neural network, which is very important in practical applications. It is also worth noticing that our approach can be easily extended to other localization technologies. This is an extended version of the conference paper published in [1].