The direction of arrival estimation of radio signal sources using convolutional neural network model for non-uniform anten array
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https://doi.org/10.54939/1859-1043.j.mst.78.2022.78-85Keywords:
Direction of Arrival; Convolutional Neural Network; Non-uniform Linear Array.Abstract
In this study, a Residual convolutional neural network model, named DOA-ResNet, is proposed to improve the direction of arrival (DOA) estimation accuracy of radio signal sources. The DOA-ResNet model is analyzed by changing the number and size of filter channels in the convolutional layer to find the dependence between the angular estimation performance and those parameters. Based on the evaluation, a suitable model is proposed for a trade-off between accuracy, model size, and execution time when it is applied in practice. In addition, the proposed model is compared with some other machine learning algorithms to demonstrate its remarkable performance in both accuracy and processing time.
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