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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
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104
Un réseau neuronal convolutif (CNN) pour l'estimation de la direction d'arrivée (DOA) à large bande de signaux électromagnétiques en champ lointain est présenté. L'algorithme proposé effectue une cartographie inverse non linéaire du signal reçu à l'angle d'arrivée. Le modèle de signal utilisé pour l'algorithme est basé sur la géométrie du réseau d'antennes circulaires et la composante de phase extraite de la matrice de covariance spatiale est utilisée comme entrée du réseau CNN. Un modèle CNN comprenant trois couches convolutives est ensuite établi pour approximer la cartographie non linéaire. Les performances du modèle CNN sont évaluées dans un environnement bruyant pour différentes valeurs de rapport signal sur bruit (SNR). Les résultats démontrent que le modèle CNN proposé avec la composante de phase de la matrice de covariance spatiale comme entrée est capable de réaliser une estimation DOA à large bande rapide et précise et atteint des performances parfaites à des valeurs SNR inférieures.
Wenli ZHU
National University of Defense Technology
Min ZHANG
National University of Defense Technology
Chenxi WU
National University of Defense Technology
Lingqing ZENG
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Wenli ZHU, Min ZHANG, Chenxi WU, Lingqing ZENG, "Broadband Direction of Arrival Estimation Based on Convolutional Neural Network" in IEICE TRANSACTIONS on Communications,
vol. E103-B, no. 3, pp. 148-154, March 2020, doi: 10.1587/transcom.2018EBP3357.
Abstract: A convolutional neural network (CNN) for broadband direction of arrival (DOA) estimation of far-field electromagnetic signals is presented. The proposed algorithm performs a nonlinear inverse mapping from received signal to angle of arrival. The signal model used for algorithm is based on the circular antenna array geometry, and the phase component extracted from the spatial covariance matrix is used as the input of the CNN network. A CNN model including three convolutional layers is then established to approximate the nonlinear mapping. The performance of the CNN model is evaluated in a noisy environment for various values of signal-to-noise ratio (SNR). The results demonstrate that the proposed CNN model with the phase component of the spatial covariance matrix as the input is able to achieve fast and accurate broadband DOA estimation and attains perfect performance at lower SNR values.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3357/_p
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@ARTICLE{e103-b_3_148,
author={Wenli ZHU, Min ZHANG, Chenxi WU, Lingqing ZENG, },
journal={IEICE TRANSACTIONS on Communications},
title={Broadband Direction of Arrival Estimation Based on Convolutional Neural Network},
year={2020},
volume={E103-B},
number={3},
pages={148-154},
abstract={A convolutional neural network (CNN) for broadband direction of arrival (DOA) estimation of far-field electromagnetic signals is presented. The proposed algorithm performs a nonlinear inverse mapping from received signal to angle of arrival. The signal model used for algorithm is based on the circular antenna array geometry, and the phase component extracted from the spatial covariance matrix is used as the input of the CNN network. A CNN model including three convolutional layers is then established to approximate the nonlinear mapping. The performance of the CNN model is evaluated in a noisy environment for various values of signal-to-noise ratio (SNR). The results demonstrate that the proposed CNN model with the phase component of the spatial covariance matrix as the input is able to achieve fast and accurate broadband DOA estimation and attains perfect performance at lower SNR values.},
keywords={},
doi={10.1587/transcom.2018EBP3357},
ISSN={1745-1345},
month={March},}
Copier
TY - JOUR
TI - Broadband Direction of Arrival Estimation Based on Convolutional Neural Network
T2 - IEICE TRANSACTIONS on Communications
SP - 148
EP - 154
AU - Wenli ZHU
AU - Min ZHANG
AU - Chenxi WU
AU - Lingqing ZENG
PY - 2020
DO - 10.1587/transcom.2018EBP3357
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E103-B
IS - 3
JA - IEICE TRANSACTIONS on Communications
Y1 - March 2020
AB - A convolutional neural network (CNN) for broadband direction of arrival (DOA) estimation of far-field electromagnetic signals is presented. The proposed algorithm performs a nonlinear inverse mapping from received signal to angle of arrival. The signal model used for algorithm is based on the circular antenna array geometry, and the phase component extracted from the spatial covariance matrix is used as the input of the CNN network. A CNN model including three convolutional layers is then established to approximate the nonlinear mapping. The performance of the CNN model is evaluated in a noisy environment for various values of signal-to-noise ratio (SNR). The results demonstrate that the proposed CNN model with the phase component of the spatial covariance matrix as the input is able to achieve fast and accurate broadband DOA estimation and attains perfect performance at lower SNR values.
ER -