A unified approach for target direction finding based on convolutional neural networks

Chong Wang; Wei Liu; Mengdi Jiang

doi:10.1109/MLSP49062.2020.9231787

A unified approach for target direction finding based on convolutional neural networks

Chong Wang, Wei Liu, Mengdi Jiang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

2 Citations (Scopus)

Abstract

A convolutional neural network (CNNs) based approach for target direction finding with the thinned coprime array (TCA) as an example is proposed. The ResNeXt network is adopted as the backbone network with a multi-label classification modification to find directions of an unknown number of targets. Unlike the traditional wisdom, where an additional co-array operation is needed for underdetermined direction finding (the number of sources is larger than the number of physical sensors), in the proposed approach, it is shown that the same network with raw data as its input can deal with both the overdetermined and underdetermined cases, although using covariance matrix of the data can reduce the complexity of the whole training process at the cost of estimation performance.

Original language	English
Title of host publication	Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728166629
DOIs	https://doi.org/10.1109/MLSP49062.2020.9231787
Publication status	Published - Oct 2020
Event	30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020 - Virtual, Espoo, Finland Duration: 21 Sept 2020 → 24 Sept 2020

Publication series

Name	IEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume	2020-September
ISSN (Print)	2161-0363
ISSN (Electronic)	2161-0371

Conference

Conference	30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020
Country/Territory	Finland
City	Virtual, Espoo
Period	21/09/20 → 24/09/20

Keywords

CNN
Multi -label classification
Target direction finding
Thinned coprime array

ASJC Scopus subject areas

Human-Computer Interaction
Signal Processing

More information

10.1109/MLSP49062.2020.9231787

Cite this

Wang, C., Liu, W., & Jiang, M. (2020). A unified approach for target direction finding based on convolutional neural networks. In Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020 Article 9231787 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2020-September). IEEE Computer Society. https://doi.org/10.1109/MLSP49062.2020.9231787

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abstract = "A convolutional neural network (CNNs) based approach for target direction finding with the thinned coprime array (TCA) as an example is proposed. The ResNeXt network is adopted as the backbone network with a multi-label classification modification to find directions of an unknown number of targets. Unlike the traditional wisdom, where an additional co-array operation is needed for underdetermined direction finding (the number of sources is larger than the number of physical sensors), in the proposed approach, it is shown that the same network with raw data as its input can deal with both the overdetermined and underdetermined cases, although using covariance matrix of the data can reduce the complexity of the whole training process at the cost of estimation performance.",

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author = "Chong Wang and Wei Liu and Mengdi Jiang",

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Wang, C, Liu, W & Jiang, M 2020, A unified approach for target direction finding based on convolutional neural networks. in Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020., 9231787, IEEE International Workshop on Machine Learning for Signal Processing, MLSP, vol. 2020-September, IEEE Computer Society, 30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020, Virtual, Espoo, Finland, 21/09/20. https://doi.org/10.1109/MLSP49062.2020.9231787

A unified approach for target direction finding based on convolutional neural networks. / Wang, Chong; Liu, Wei; Jiang, Mengdi.
Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. IEEE Computer Society, 2020. 9231787 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2020-September).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - A convolutional neural network (CNNs) based approach for target direction finding with the thinned coprime array (TCA) as an example is proposed. The ResNeXt network is adopted as the backbone network with a multi-label classification modification to find directions of an unknown number of targets. Unlike the traditional wisdom, where an additional co-array operation is needed for underdetermined direction finding (the number of sources is larger than the number of physical sensors), in the proposed approach, it is shown that the same network with raw data as its input can deal with both the overdetermined and underdetermined cases, although using covariance matrix of the data can reduce the complexity of the whole training process at the cost of estimation performance.

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Wang C, Liu W, Jiang M. A unified approach for target direction finding based on convolutional neural networks. In Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. IEEE Computer Society. 2020. 9231787. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). doi: 10.1109/MLSP49062.2020.9231787

A unified approach for target direction finding based on convolutional neural networks

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Keywords

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