Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR
Signal source number detection is an essential issue for the direction of arrival (DOA) estimation in satellite communication systems. The performances of conventional and deep-learning-based signal source number detection methods will deteriorate when the signal-to-noise ratio is low or coherent si...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9915600/ |
| _version_ | 1798029271141187584 |
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| author | Yunfeng Li Yonghui Huang Jian Ren Ying Liu Gert Frolund Pedersen Ming Shen |
| author_facet | Yunfeng Li Yonghui Huang Jian Ren Ying Liu Gert Frolund Pedersen Ming Shen |
| author_sort | Yunfeng Li |
| collection | DOAJ |
| description | Signal source number detection is an essential issue for the direction of arrival (DOA) estimation in satellite communication systems. The performances of conventional and deep-learning-based signal source number detection methods will deteriorate when the signal-to-noise ratio is low or coherent signals exist. This paper proposes a DOA detection network (DTN) combined with the root weighted subspace fitting (root-WSF) method to tackle this challenge. The DTN uses the constructed deep neural networks (DNN) to denoise the received signals and captures the nonlinear mapping relationship between the received signals and the number of signal sources. The received signals in the complex-valued domain are directly treated as DTN’s input, and the label of DTN is the one-hot encoding of the source number. It solves the issue that the classifier cannot well-handle the coherent signals and extends the values of discrete features to Euclidean space. Accordingly, the trained DTN can detect the signal source numbers with an average detection accuracy of 96.6%, and the root-WSF algorithm is applied as the rear stage of DOA estimation. Compared with the traditional DOA methods, the proposed DTN incorporated with the root-WSF algorithm features superior robustness, high DOA estimation accuracy, and enhanced resolution. |
| first_indexed | 2024-04-11T19:21:29Z |
| format | Article |
| id | doaj.art-de021c40c4eb48acafc4ea03f03bebc2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T19:21:29Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-de021c40c4eb48acafc4ea03f03bebc22022-12-22T04:07:17ZengIEEEIEEE Access2169-35362022-01-011010998310999310.1109/ACCESS.2022.32137129915600Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNRYunfeng Li0https://orcid.org/0000-0002-5415-7420Yonghui Huang1https://orcid.org/0000-0002-9745-4177Jian Ren2https://orcid.org/0000-0001-9899-5963Ying Liu3https://orcid.org/0000-0001-7521-5880Gert Frolund Pedersen4https://orcid.org/0000-0002-6570-7387Ming Shen5https://orcid.org/0000-0002-9388-3513National Space Science Center, Chinese Academy of Sciences, Beijing, ChinaNational Space Science Center, Chinese Academy of Sciences, Beijing, ChinaNational Key Laboratory of Antennas and Microwave Technology, Xidian University, Shaanxi, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, ChinaDepartment of Electronic Systems, Aalborg University, Aalborg, DenmarkDepartment of Electronic Systems, Aalborg University, Aalborg, DenmarkSignal source number detection is an essential issue for the direction of arrival (DOA) estimation in satellite communication systems. The performances of conventional and deep-learning-based signal source number detection methods will deteriorate when the signal-to-noise ratio is low or coherent signals exist. This paper proposes a DOA detection network (DTN) combined with the root weighted subspace fitting (root-WSF) method to tackle this challenge. The DTN uses the constructed deep neural networks (DNN) to denoise the received signals and captures the nonlinear mapping relationship between the received signals and the number of signal sources. The received signals in the complex-valued domain are directly treated as DTN’s input, and the label of DTN is the one-hot encoding of the source number. It solves the issue that the classifier cannot well-handle the coherent signals and extends the values of discrete features to Euclidean space. Accordingly, the trained DTN can detect the signal source numbers with an average detection accuracy of 96.6%, and the root-WSF algorithm is applied as the rear stage of DOA estimation. Compared with the traditional DOA methods, the proposed DTN incorporated with the root-WSF algorithm features superior robustness, high DOA estimation accuracy, and enhanced resolution.https://ieeexplore.ieee.org/document/9915600/Deep neural network (DNN)direction of arrival (DOA)DOA detection network (DTN)root weighted subspace fitting (root-WSF)satellite communication system |
| spellingShingle | Yunfeng Li Yonghui Huang Jian Ren Ying Liu Gert Frolund Pedersen Ming Shen Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR IEEE Access Deep neural network (DNN) direction of arrival (DOA) DOA detection network (DTN) root weighted subspace fitting (root-WSF) satellite communication system |
| title | Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR |
| title_full | Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR |
| title_fullStr | Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR |
| title_full_unstemmed | Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR |
| title_short | Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR |
| title_sort | robust doa estimation in satellite systems in presence of coherent signals subject to low snr |
| topic | Deep neural network (DNN) direction of arrival (DOA) DOA detection network (DTN) root weighted subspace fitting (root-WSF) satellite communication system |
| url | https://ieeexplore.ieee.org/document/9915600/ |
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