An Intelligent Direction Finding Method with Deep Neural Network

Xu, Yang; Guo, Huayan; Fan, Rong; Si, Chengke; Ding, Xiaoqing

doi:10.1109/iccasit50869.2020.9368549

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…On the contrary, [42] combines an adversarial network with a ResNet and mitigates the mutual coupling. As discussed in [43], a ResNet classifier achieves notable DOA predictions from multiple directions, whereas [44] merges a ResNet with an autoencoder for reliable estimations in noisy environments. Finally, in [45] a deep ResNet setup leverages a co-prime pair of ULAs to alleviate the impact of imperfections.…”

Section: Prior Workmentioning

confidence: 99%

Improving DOA Estimation via an Optimal Deep Residual Neural Network Classifier on Uniform Linear Arrays

Kassir,

Kantartzis,

Lazaridis

et al. 2024

IEEE Open J. Antennas Propag.

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The main objective of this work is to improve and evaluate the effectiveness of the neural network (NN) architecture in the domain of estimation of direction of arrival (DOA), with an emphasis on a multi-class classification task with grid resolutions of 0.25 and 0.1. Specifically, a comprehensive assessment is performed to determine the competence of a residual NN (ResNet) in predicting the angle of arrival (AOA) of intercepted signals. Such signals are received by a 16-element uniform linear array and are subjected to real-world noise conditions. To this end, the superiority of the ResNet architecture in DOA estimations is substantiated through a comparison analysis with two other highly recognized NNs, namely, the feed-forward NN and the convolutional NN. Numerical results indicate that the ResNet model exhibits notable precision in estimating the AOAs, across various classes within a broad spectrum, along with a rapid temporal response. Finally, it remains consistent and maintains its superior performance even for diverse incoming signals and significantly reduced SNRs.INDEX TERMS Antenna array analysis and synthesis, antenna optimization, machine learning, Directionof-arrival (DOA) estimation, residual neural networks,

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