Super-resolution DOA estimation for correlated off-grid signals via deep estimator

doi:10.21629/JSEE.2022.00074

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (6): 1096-1107.doi: 10.21629/JSEE.2022.00074

• ELECTRONICS TECHNOLOGY • Previous Articles

Super-resolution DOA estimation for correlated off-grid signals via deep estimator

Shuang WU¹^,*(), Ye YUAN²(), Weike ZHANG²(), Naichang YUAN²()

¹ Facility Design and Instrumentation Institute, China Aerodynamics Research and Development Center, Mianyang 621000, China
² State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China

Received:2021-02-24 Online:2022-12-18 Published:2022-12-24
Contact: Shuang WU E-mail:ws02114006@163.com;767411434@qq.com;weikeleixd@163.com;yuannaichang@hotmail.com
About author:
WU Shuang was born in 1993. He received his M.S. and Ph.D. degrees in electromagnetic field and microwave technique from National University of Defense Technology, Changsha, China in 2017 and 2021, respectively. He is currently working Facility Design and Instrumentation Institute, China Aerodynamics Research and Development Centre, Mianyand, China. His research interests include array signal processing and machine learning. E-mail: ws02114006@163.com

YUAN Ye was born in 1994. He received his M.S. degree in electromagnetic field and microwave technique from National University of Defense Technology, Changsha, China in 2019. He is currently working towards his Ph.D. degree in National University of Defense Technology. His research interests include passive microwave circuits design and array signal processing. E-mail: 767411434@qq.com

ZHANG Weike was born in 1991. He received his M.S. degree in electromagnetic field and microwave technique from National University of Defense Technology, Changsha, China in 2017. He is currently working towards his Ph.D. degree in National University of Defense Technology. His research interests include microwave and array signal processing. E-mail: weikeleixd@163.com

YUAN Naichang was born in 1965. He received his M.S. and Ph.D. degrees in electronic science and technology from University of Electronic Science and Technology of China, Chendu, China in 1991 and 1994, respectively. He is currently working in the College of Electronic Science, National University of Defense Technology. His research interests include array signal processing, SAR imaging processing and signal processing in radar, antenna theory and design. E-mail: yuannaichang@hotmail.com

Abstract

Abstract:

This paper develops a deep estimator framework of deep convolution networks (DCNs) for super-resolution direction of arrival (DOA) estimation. In addition to the scenario of correlated signals, the quantization errors of the DCN are the major challenge. In our deep estimator framework, one DCN is used for spectrum estimation with quantization errors, and the remaining two DCNs are used to estimate quantization errors. We propose training our estimator using the spatial sampled covariance matrix directly as our deep estimator’s input without any feature extraction operation. Then, we reconstruct the original spatial spectrum from the spectrum estimate and quantization errors estimate. Also, the feasibility of the proposed deep estimator is analyzed in detail in this paper. Once the deep estimator is appropriately trained, it can recover the correlated signals’ spatial spectrum fast and accurately. Simulation results show that our estimator performs well in both resolution and estimation error compared with the state-of-the-art algorithms.

Key words: off-grid direction of arrival (DOA) estimation, deep convolution network (DCN), correlated signal, quantization error, super-resolution

Shuang WU, Ye YUAN, Weike ZHANG, Naichang YUAN. Super-resolution DOA estimation for correlated off-grid signals via deep estimator[J]. Journal of Systems Engineering and Electronics, 2022, 33(6): 1096-1107.

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References 35

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Execution time	Algorithm
Execution time	ROOT-MUSIC	ESPRIT	MODE	SBL	Proposed
Uncorrelated	5.44×10⁻⁴	4.23×10⁻⁴	8.52×10⁻⁴	0.56	1.41×10⁻⁴
Correlated	5.69×10⁻⁴	5.10×10⁻⁴	9.16×10⁻⁴	0.69	1.41×10⁻⁴

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Super-resolution DOA estimation for correlated off-grid signals via deep estimator

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