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Wiometrics : Comparative Performance of Artificial Neural Networks for Wireless Navigation

Whiton, Russ LU ; Chen, Junshi LU and Tufvesson, Fredrik LU orcid (2024) In IEEE Transactions on Vehicular Technology p.1-16
Abstract

Radio signals are used broadly as navigation aids, and current and future terrestrial wireless communication systems have properties that make their dual-use for this purpose attractive. Sub-6 GHz carrier frequencies enable widespread coverage for data communication and navigation, but typically offer smaller bandwidths and limited resolution for precise estimation of geometries, particularly in environments where propagation channels are diffuse in time and/or space. Nonparametric methods have been employed with some success for such scenarios both commercially and in literature, but often with an emphasis on low-cost hardware and simple models of propagation, or with simulations that do not fully capture hardware... (More)

Radio signals are used broadly as navigation aids, and current and future terrestrial wireless communication systems have properties that make their dual-use for this purpose attractive. Sub-6 GHz carrier frequencies enable widespread coverage for data communication and navigation, but typically offer smaller bandwidths and limited resolution for precise estimation of geometries, particularly in environments where propagation channels are diffuse in time and/or space. Nonparametric methods have been employed with some success for such scenarios both commercially and in literature, but often with an emphasis on low-cost hardware and simple models of propagation, or with simulations that do not fully capture hardware impairments and complex propagation mechanisms. In this article, we make opportunistic observations of downlink signals transmitted by commercial cellular networks by using a software-defined radio and massive antenna array mounted on a ground vehicle in an urban non line-of-sight scenario, together with a ground truth reference for vehicle pose. With these observations as inputs, we employ artificial neural networks to generate estimates of vehicle location and heading for various artificial neural network architectures and different representations of the input observation data, which we call wiometrics, and compare the performance for navigation. Position accuracy on the order of a few meters, and heading accuracy of a few degrees, are achieved for the best-performing combinations of networks and wiometrics. Based on the results of the experiments we draw conclusions regarding possible future directions for wireless navigation using statistical methods.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
in press
subject
keywords
Artificial neural networks, Artificial Neural Networks, Channel Estimation, Fingerprint recognition, Hardware, Navigation, Radio navigation, Radiowave Propagation, Wireless communication, Wireless sensor networks
in
IEEE Transactions on Vehicular Technology
pages
16 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85192161329
ISSN
0018-9545
DOI
10.1109/TVT.2024.3396286
language
English
LU publication?
yes
id
7833043d-580f-4a01-bc75-2558bb51c887
date added to LUP
2024-05-15 13:08:27
date last changed
2024-05-15 13:09:25
@article{7833043d-580f-4a01-bc75-2558bb51c887,
  abstract     = {{<p>Radio signals are used broadly as navigation aids, and current and future terrestrial wireless communication systems have properties that make their dual-use for this purpose attractive. Sub-6 GHz carrier frequencies enable widespread coverage for data communication and navigation, but typically offer smaller bandwidths and limited resolution for precise estimation of geometries, particularly in environments where propagation channels are diffuse in time and&amp;#x002F;or space. Nonparametric methods have been employed with some success for such scenarios both commercially and in literature, but often with an emphasis on low-cost hardware and simple models of propagation, or with simulations that do not fully capture hardware impairments and complex propagation mechanisms. In this article, we make opportunistic observations of downlink signals transmitted by commercial cellular networks by using a software-defined radio and massive antenna array mounted on a ground vehicle in an urban non line-of-sight scenario, together with a ground truth reference for vehicle pose. With these observations as inputs, we employ artificial neural networks to generate estimates of vehicle location and heading for various artificial neural network architectures and different representations of the input observation data, which we call wiometrics, and compare the performance for navigation. Position accuracy on the order of a few meters, and heading accuracy of a few degrees, are achieved for the best-performing combinations of networks and wiometrics. Based on the results of the experiments we draw conclusions regarding possible future directions for wireless navigation using statistical methods.</p>}},
  author       = {{Whiton, Russ and Chen, Junshi and Tufvesson, Fredrik}},
  issn         = {{0018-9545}},
  keywords     = {{Artificial neural networks; Artificial Neural Networks; Channel Estimation; Fingerprint recognition; Hardware; Navigation; Radio navigation; Radiowave Propagation; Wireless communication; Wireless sensor networks}},
  language     = {{eng}},
  pages        = {{1--16}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Vehicular Technology}},
  title        = {{Wiometrics : Comparative Performance of Artificial Neural Networks for Wireless Navigation}},
  url          = {{http://dx.doi.org/10.1109/TVT.2024.3396286}},
  doi          = {{10.1109/TVT.2024.3396286}},
  year         = {{2024}},
}