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Abstract
Ultra-Wide Bandwidth (UWB) and mm-wave radio systems can resolve specular multipath components (SMCs) from estimated channel impulse response measurements. A geometric model can describe the delays, angles-of-arrival, and angles-of-departure of these SMCs, allowing for a prediction of these channel features. For the modeling of the amplitudes of the SMCs, a data-driven approach has been proposed recently, using Gaussian Process Regression (GPR) to map and predict the SMC amplitudes. In this paper, the applicability of the proposed multipath-resolved, GPR-based channel model is analyzed by studying features of the propagation channel from a set of channel measurements. The features analyzed include the energy capture of the modeled SMCs, the number of resolvable SMCs, and the ranging information that could be extracted from the SMCs. The second contribution of the paper concerns the potential applicability of the channel model for a multipath-resolved, single-anchor positioning system. The predicted channel knowledge is used to evaluate the measurement likelihood function at candidate positions throughout the environment. It is shown that the environmental awareness created by the multipath-resolved, GPR-based channel model yields higher robustness against position estimation outliers.
Original language | English |
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Article number | 462 |
Number of pages | 22 |
Journal | Sensors |
Volume | 22 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Jan 2022 |
Keywords
- Environmental awareness
- Gaussian process regression
- Geometry-based channel modeling
- Location awareness
- Mm-wave radios
- Multipath-assisted positioning
- Single-anchor positioning
- Situational awareness
- UWB radios
ASJC Scopus subject areas
- Analytical Chemistry
- Information Systems
- Instrumentation
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering
- Biochemistry
Fields of Expertise
- Information, Communication & Computing
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Dive into the research topics of 'Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization'. Together they form a unique fingerprint.Projects
- 1 Finished
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Dependable Internet of Things
Boano, C. A., Kubin, G., Bloem, R., Horn, M., Pernkopf, F., Zakany, N., Mangard, S., Witrisal, K., Römer, K. U., Aichernig, B., Bösch, W., Baunach, M. C., Tappler, M., Malenko, M., Weiser, S., Eichlseder, M., Leitinger, E., Grosinger, J., Großwindhager, B., Ebrahimi, M., Alothman Alterkawi, A. B., Knoll, C., Teschl, R., Saukh, O., Rath, M., Steinberger, M., Steinbauer-Wagner, G. & Tranninger, M.
1/01/16 → 31/03/22
Project: Research project