Near-field localization using machine learning: An empirical study

Mikko Laakso, Risto Wichman

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

17 Downloads (Pure)

Abstract

Estimation methods for passive near-field localization have been studied to an appreciable extent in signal processing research. Such localization methods find use in various applications, for instance in medical imaging. However, methods based on the standard near-field signal model can be inaccurate in real-world applications, due to deficiencies of the model itself and hardware imperfections. It is expected that deep neural network (DNN) based estimation methods trained on the nonideal sensor array signals could outperform the model-driven alternatives. In this work, a DNN based estimator is trained and validated on a set of real world measured data. The series of measurements was conducted with an inexpensive custom built multichannel software-defined radio (SDR) receiver, which makes the nonidealities more prominent. The results show that a DNN based localization estimator clearly outperforms the compared model-driven method.

Original languageEnglish
Title of host publication2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
PublisherIEEE
Number of pages5
ISBN (Electronic)9781728189642
DOIs
Publication statusPublished - Apr 2021
MoE publication typeA4 Article in a conference publication
EventIEEE Vehicular Technology Conference - Virtual, Online
Duration: 25 Apr 202128 Apr 2021
Conference number: 93

Publication series

NameIEEE Vehicular Technology Conference
Volume2021-April
ISSN (Print)1090-3038
ISSN (Electronic)2577-2465

Conference

ConferenceIEEE Vehicular Technology Conference
Abbreviated titleVTC-Spring
CityVirtual, Online
Period25/04/202128/04/2021

Keywords

  • deep learning
  • near-field localization
  • SDR

Fingerprint

Dive into the research topics of 'Near-field localization using machine learning: An empirical study'. Together they form a unique fingerprint.

Cite this