Wiometrics : Comparative Performance of Artificial Neural Networks for Wireless Navigation
(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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- author
- Whiton, Russ LU ; Chen, Junshi LU and Tufvesson, Fredrik LU
- organization
- publishing date
- 2024
- 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&#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}}, }