title: Information Extraction for Data-Driven Indoor Tracking with Ultra-Wideband Signals
title: Informationsextraktion für datengetriebenes Indoor-Tracking mit Ultra-Breitbandsignalen
Creators: name: Kram, Sebastian
Publisher: name: Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Year: 2024
Subjects: subject: RF Channels
subject: Machine Learning
subject: Navigation and Positioning
subject: Channel Measurements
subject: Unsupervised learning

description: In indoor environments, emerging applications such robot navigation or industrial process surveillance rely on accurate radio frequency ( RF)-based tracking. It employs the commu- nication links between mobile agents and stationary anchors to infer their position. A major challenge in classical multilateration-based approaches is the influence of the environment on signal propagation, as furniture and structural components cause multipath propagation like scattering and reflections. As a solution, ultra-wideband (UWB )-signalling resolves multipath components ( MPCs) by collecting channel measurements (CMs) that contain additional spa- tial spatial information on the propagation. This has the potential to reduce deployment costs for RF-based tracking systems as it lowers the necessary anchor infrastructure. To exploit this information for tracking, however, it first has to be extracted from the CMs. The limits of the channel hereby cause overlap between MPCs and non-linear distortions. State-of-the-art data-driven machine learning ( ML ) methods, especially neural networks ( NNs), learn efficient function approximations from training data that can handle these non-linear effects. However, for cost-efficient deployment of these data-driven information extraction models to a target en- vironment, they need to generalize from environment-representative training data. This thesis proposes different NNs that learn to extract spatial information in industrial environments with harsh propagation conditions from data obtained in different environments. Specifically, these models learn how to estimate the line-of-sight ( LOS) presence based on data from artificial labeling environments using convolutional neural networks (CNNs) and variational autoencoders (VAE s)-based anomality detection. Because CM contains an arbitrary num- ber of MPC, MPC delay extraction is not a straightforward classification or regression task and state-of-the-art methods rely on computationally expensive iterative statistical signal processing. Instead, this thesis proposes a method that learns to accurately extract MPC delays from simulated data with a time-series segmentation approach relying on a compu- tationally efficient UNET CNN. A lower-dimensional representation of CM is achieved with propagation-model inspired features in the state-of-the-art. As a data-driven alternative, this thesis proposes a representation learning approach that learns to compress the spatial information in CM into a lower-dimensional latent-space representation from data obtained in similar industrial environments with an autoencoder (AE). Tracking results prove that this representation results in more accurate tracking than state-of-the-art features. Thus, compared to the state-of-the-art, the proposed spatial information extraction learn to accurately represent the spatial information in CM from easily obtainable data. The generalization abilities of the proposed methods reduce the need for cost- and labor-intensive data collection and enable the deployment of trained models to an application environments with harsh propagation conditions. Furthermore, this information extraction compresses high-dimensional CMs into a lower-dimensional representation that reduces data storage and transmission requirements in a CM-based tracking system. To exploit this extracted information for tracking, this thesis proposes a particle filter for fingerprinting (FP) that fuses the extracted LOS presence indicator and a Gaussian process regression (GPR)-based observation likelihood model of the extracted latent-space representation. As it employs this additional, compressed spatial information it enables accurate tracking in an indoor environments with harsh propagation conditions, unlike state-of-the-art LOS-focused tracking methods. FP is trained on environment-specific data that are hard to acquire and maintain. The proposed tracking methods lowers the data collection and maintenance effort over state-of-the-art CM-based FP because the learned observation likelihood model implies a reliability measure, and thus can learn from spatially sparse datasets that only contain data instances in areas within the environment that require FP for accurate tracking. Real-world evaluation shows that the proposed tracking method achieves more reliable information extraction and tracking than state-of-the-art methods, especially on a sparse FP database and with a small anchor infrastructure. The proposed information extraction reduces transmission and data storage requirements and generalizes to target environments at low data acquisition effort. So, is enables cost- and energy effi- cient deployment of data-driven CM-based positioning in indoor environments with harsh propagation conditions.