Robust DoA Estimation Using Denoising Autoencoder and Deep Neural Networks

As one of the most critical technology in array signal processing, direction of arrival (DoA) estimation has received a great deal of attention in many areas. Traditional methods perform well when the signal-to-noise ratio (SNR) is high and the receiving array is perfect, which are quite different f...

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Published in:	IEEE access Vol. 10; pp. 52551 - 52564
Main Authors:	Chen, Dawei, Shi, Shuo, Gu, Xuemai, Shim, Byonghyo
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Array signal processing Arrays Artificial neural networks Covariance matrices deep neural networks denoising autoencoder Direction of arrival Direction-of-arrival estimation DoA Estimation Feature extraction Machine learning Mutual coupling Neural networks Noise reduction Robustness Signal processing Signal to noise ratio SNR
ISSN:	2169-3536, 2169-3536
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Abstract	As one of the most critical technology in array signal processing, direction of arrival (DoA) estimation has received a great deal of attention in many areas. Traditional methods perform well when the signal-to-noise ratio (SNR) is high and the receiving array is perfect, which are quite different from the situation in some real applications (e.g., the marine communication scenario). To get satisfying performance of DoA estimation when SNR is low and the array is inaccurate (mutual coupling exist), this paper introduces a scheme consisting of denoising autoencoder (DAE) and deep neural networks (DNN), referred to as DAE-DNN scheme. DAE is used to reconstruct a clean "repaired" input from its corrupted version to increase the robustness, and then divide the input into multiple parts in different sub-areas. DNN is used to learn the mapping between the received signals and the refined grids of angle in each sub-areas, then the outputs of each sub-areas are concatenated to perform the final DoA estimation. By simulations in different SNR regimes, we study the performance of DAE-DNN in terms of the different snapshots, batch size, learning rate, and epoch. Our results demonstrate that the proposed DAE-DNN scheme outperforms traditional methods in accuracy and robustness.
AbstractList	As one of the most critical technology in array signal processing, direction of arrival (DoA) estimation has received a great deal of attention in many areas. Traditional methods perform well when the signal-to-noise ratio (SNR) is high and the receiving array is perfect, which are quite different from the situation in some real applications (e.g., the marine communication scenario). To get satisfying performance of DoA estimation when SNR is low and the array is inaccurate (mutual coupling exist), this paper introduces a scheme consisting of denoising autoencoder (DAE) and deep neural networks (DNN), referred to as DAE-DNN scheme. DAE is used to reconstruct a clean "repaired" input from its corrupted version to increase the robustness, and then divide the input into multiple parts in different sub-areas. DNN is used to learn the mapping between the received signals and the refined grids of angle in each sub-areas, then the outputs of each sub-areas are concatenated to perform the final DoA estimation. By simulations in different SNR regimes, we study the performance of DAE-DNN in terms of the different snapshots, batch size, learning rate, and epoch. Our results demonstrate that the proposed DAE-DNN scheme outperforms traditional methods in accuracy and robustness.
Author	Gu, Xuemai Chen, Dawei Shi, Shuo Shim, Byonghyo
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SubjectTerms	Array signal processing Arrays Artificial neural networks Covariance matrices deep neural networks denoising autoencoder Direction of arrival Direction-of-arrival estimation DoA Estimation Feature extraction Machine learning Mutual coupling Neural networks Noise reduction Robustness Signal processing Signal to noise ratio SNR
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Title	Robust DoA Estimation Using Denoising Autoencoder and Deep Neural Networks
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