Abstract
The objective of this study is to analyze and compare different neural network (NN) architectures as multi-class classifiers to estimate the direction of arrival (DOA) using a uniform linear array (ULA). The study specifically investigates the prediction skills of three NNs: feed forward NN (FFNN), convolutional NN (CNN), and residual NN (ResNet) when estimating incoming signal DOAs in a realistic ULA of (M = 16) elements under noisy conditions. The NNs are trained on a correlation matrix generated by a ULA to estimate the DOAs. The results of the simulations indicate that ResNet performs better than FFNN and CNN in accurately estimating incoming signals.
Original language | English |
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Title of host publication | 2023 12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Number of pages | 4 |
ISBN (Electronic) | 9798350321074 |
ISBN (Print) | 9798350321081 |
DOIs | |
Publication status | Published - 17 Jul 2023 |
Event | 12th International Conference on Modern Circuits and Systems Technologies - Athens, Greece Duration: 28 Jun 2023 → 30 Jun 2023 Conference number: 12 |
Conference
Conference | 12th International Conference on Modern Circuits and Systems Technologies |
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Abbreviated title | MOCAST 2023 |
Country/Territory | Greece |
City | Athens |
Period | 28/06/23 → 30/06/23 |