Yenilemeli Derin Öğrenme ile GNSS Bandında Uyarlanabilir Hüzme Biçimlendirme

Translated title of the contribution: Adaptive Beamforming Based on Recurrent Deep Learning for GNSS Bands

İrem Aras*, Işın Erer, Eren Akdemir*

*Corresponding author for this work

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

Abstract

Global Navigation Satellite Systems (GNSS) are undoubtedly the most preferred navigation method in these days. However, GNSS signals can frequently become a target for undesired jammer signals. The most known solution for this problem is beamforming methods. In this study, recurrent neural network (RNN) structures are used to determine the beamforming coefficients of signals with known angle of arrivals. A new encoder-decoder (ED) beamforming approach is presented, which is built with long short-term memory (LSTM) cells. ED beamforming approach is compared with a previously proposed multilayer LSTM network and known null steering beamforming (NSB), which ED beamforming is resulted more successfully in terms of beam and null divergences in directions of signal arrivals and with a shorter response time.

Translated title of the contributionAdaptive Beamforming Based on Recurrent Deep Learning for GNSS Bands
Original languageTurkish
Title of host publication32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350388961
DOIs
Publication statusPublished - 2024
Event32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Mersin, Turkey
Duration: 15 May 202418 May 2024

Publication series

Name32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings

Conference

Conference32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024
Country/TerritoryTurkey
CityMersin
Period15/05/2418/05/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

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