Adaptive Beamforming applied for signals estimated with direction-of-arrival algorithms from the ESPRIT family

Iozsa, Arpad

doi:10.1109/isetc.2012.6408151

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Mainly, it can be attained through beamforming techniques, yet their computational complexity is proportional to the source number. On the other hand, an interesting candidate is the Capon and Bartlett scheme [2], while the multiple signal classification (MUSIC) method and the estimation of signal parameters via rotational invariance tech-niques (ESPRIT) are also widely acknowledged. Explicitly, the former can yield unbiased estimations; nevertheless, it is vulnerable when the signals have strong correlations [3]- [5].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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Simulation of Human Ear Recognition Sound Direction Based on Convolutional Neural Network

Wang

et al. 2020

Journal of Intelligent Systems

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In recent years, more and more people are applying Convolutional Neural Networks to the study of sound signals. The main reason is the translational invariance of convolution in time and space. Thereby the diversity of the sound signal can be overcome. However, in terms of sound direction recognition, there are also problems such as a microphone matrix being too large, and feature selection. This paper proposes a sound direction recognition using a simulated human head with microphones at both ears. Theoretically, the two microphones cannot distinguish the front and rear directions. However, we use the original data of the two channels as the input of the convolutional neural network, and the resolution effect can reach more than 0.9. For comparison, we also chose the delay feature (GCC) for sound direction recognition. Finally, we also conducted experiments that used probability distributions to identify more directions.

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Adaptive Beamforming applied for signals estimated with direction-of-arrival algorithms from the ESPRIT family

Cited by 2 publications

References 5 publications

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

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

Simulation of Human Ear Recognition Sound Direction Based on Convolutional Neural Network

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