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 contribution | Adaptive Beamforming Based on Recurrent Deep Learning for GNSS Bands |
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Original language | Turkish |
Title of host publication | 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9798350388961 |
DOIs | |
Publication status | Published - 2024 |
Event | 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Mersin, Turkey Duration: 15 May 2024 → 18 May 2024 |
Publication series
Name | 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings |
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Conference
Conference | 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 |
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Country/Territory | Turkey |
City | Mersin |
Period | 15/05/24 → 18/05/24 |
Bibliographical note
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