Single-anchor UWB Localization using Channel Impulse Response Distributions

TL;DR

This paper introduces a novel single-anchor UWB localization method that leverages statistical features of channel impulse responses using Gaussian mixture models to improve accuracy in indoor environments.

Contribution

It proposes a new approach that learns joint distributions of CIR amplitudes and introduces a set-based similarity metric for enhanced localization accuracy.

Findings

Improved localization accuracy over traditional methods.

Effective use of Gaussian mixture models for CIR feature learning.

Enhanced performance with set-based similarity metrics.

Abstract

Ultra-wideband (UWB) devices are widely used in indoor localization scenarios. Single-anchor UWB localization shows advantages because of its simple system setup compared to conventional two-way ranging (TWR) and trilateration localization methods. In this work, we focus on single-anchor UWB localization methods that learn statistical features of the channel impulse response (CIR) in different location areas using a Gaussian mixture model (GMM). We show that by learning the joint distributions of the amplitudes of different delay components, we achieve a more accurate location estimate compared to considering each delay bin independently. Moreover, we develop a similarity metric between sets of CIRs. With this set-based similarity metric, we can further improve the estimation performance, compared to treating each snapshot separately. We showcase the advantages of the proposed methods in multiple application scenarios.