Estimating TOA Reliability with Variational Autoencoders

Stahlke M, Kram S, Ott F, Feigl T, Mutschler C (2021)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2021

Journal

IEEE Sensors Journal Institute of Electrical and Electronics Engineers (IEEE)

Pages Range: 1-6

DOI: 10.1109/JSEN.2021.3101933

Open Access Link: https://ieeexplore.ieee.org/document/9503384

Abstract

Radio frequency (RF)-based localization yields centimeter-accurate positions under mild propagation conditions. However, propagation conditions predominant in indoor environments (e.g. industrial production) are often challenging as signal blockage, diffraction and dense multipath lead to errors in the time of flight (TOF) estimation and hence to a degraded localization accuracy. A major topic in high-precision RF-based localization is the identification of such anomalous signals that negatively affect the localization performance, and to mitigate the errors introduced by them. As such signal and error characteristics depend on the environment, data-driven approaches have shown to be promising. However, there is a trade-off to a bad generalization and a need for an extensive and time-consuming recording of training data associated with it. We propose to use generative deep learning models for out-of-distribution detection based on channel impulse responses (CIRs). We use a Variational Autoencoder (VAE) to predict an anomaly score for the channel of a TOF-based Ultra-wideband (UWB) system. Our experiments show that a VAE trained only on line-of-sight (LOS) training data generalizes well to new environments and detects non-line-of-sight CIRs with an accuracy of 85%. We also show that integrating our anomaly score into a TOF-based extended Kalman filter (EKF) improves tracking performance by over 25%.

Authors with CRIS profile

Maximilian Stahlke
Sebastian Kram Lehrstuhl für Informationstechnik mit dem Schwerpunkt Kommunikationselektronik (Stiftungslehrstuhl) Tobias Feigl Lehrstuhl für Informatik 2 (Programmiersysteme) Christopher Mutschler

Related research project(s)

Recurrent Neuronal Networks (RNNs) for Real-Time Estimation of Nonlinear Motion Models (RuNN) Oct. 1, 2017 - March 31, 2021

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Stahlke, M., Kram, S., Ott, F., Feigl, T., & Mutschler, C. (2021). Estimating TOA Reliability with Variational Autoencoders. IEEE Sensors Journal, 1-6. https://dx.doi.org/10.1109/JSEN.2021.3101933

MLA:

Stahlke, Maximilian, et al. "Estimating TOA Reliability with Variational Autoencoders." IEEE Sensors Journal (2021): 1-6.

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