Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation
Source enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However,...
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MDPI AG
2021-02-01
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| Online Access: | https://www.mdpi.com/2076-3417/11/4/1942 |
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| author | Yunseong Lee Chanhong Park Taeyoung Kim Yeongyoon Choi Kiseon Kim Dongho Kim Myung-Sik Lee Dongkeun Lee |
| author_facet | Yunseong Lee Chanhong Park Taeyoung Kim Yeongyoon Choi Kiseon Kim Dongho Kim Myung-Sik Lee Dongkeun Lee |
| author_sort | Yunseong Lee |
| collection | DOAJ |
| description | Source enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However, they have shortcomings such as the imperfect accuracy even at a high signal-to-noise ratio (SNR), the poor performance at low SNR, and the limited detection number of sources. This paper proposestwo source enumeration approaches using the ratio of eigenvalue gaps and the threshold trained by a machine learning based clustering algorithm for gaps of normalized eigenvalues, respectively. In the first approach, a criterion formula derived with eigenvalue gaps is used to determine the number of sources, where the formula has maximum value. In the second approach, datasets of normalized eigenvalue gaps are generated for the machine learning based clustering algorithm and the optimal threshold for estimation of the number of sources are derived, which minimizes source enumeration error probability. Simulation results show that our proposed approaches are superior to the conventional approaches from both the estimation accuracy and numerical detectability extent points of view. The results demonstrate that the second proposed approach has the feasibility to improve source enumeration performance if appropriate learning datasets are sufficiently provided. |
| first_indexed | 2024-03-09T00:37:47Z |
| format | Article |
| id | doaj.art-c0f155e4acd04ee5b1823d688f7eecc5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T00:37:47Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c0f155e4acd04ee5b1823d688f7eecc52023-12-11T18:03:12ZengMDPI AGApplied Sciences2076-34172021-02-01114194210.3390/app11041942Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival EstimationYunseong Lee0Chanhong Park1Taeyoung Kim2Yeongyoon Choi3Kiseon Kim4Dongho Kim5Myung-Sik Lee6Dongkeun Lee7Electronic Warfare Research Center, Gwangju Institute of Science and Technology, Gwangju 61005, KoreaElectronic Warfare Research Center, Gwangju Institute of Science and Technology, Gwangju 61005, KoreaElectronic Warfare Research Center, Gwangju Institute of Science and Technology, Gwangju 61005, KoreaSchool of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, KoreaSchool of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, KoreaElectronic Warfare R&D Center, LIG Nex1 Co., Ltd., Yongin-si 16911, KoreaElectronic Warfare R&D Center, LIG Nex1 Co., Ltd., Yongin-si 16911, Korea2nd R&D Institute, Agency for Defense Development, Daejeon 34186, KoreaSource enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However, they have shortcomings such as the imperfect accuracy even at a high signal-to-noise ratio (SNR), the poor performance at low SNR, and the limited detection number of sources. This paper proposestwo source enumeration approaches using the ratio of eigenvalue gaps and the threshold trained by a machine learning based clustering algorithm for gaps of normalized eigenvalues, respectively. In the first approach, a criterion formula derived with eigenvalue gaps is used to determine the number of sources, where the formula has maximum value. In the second approach, datasets of normalized eigenvalue gaps are generated for the machine learning based clustering algorithm and the optimal threshold for estimation of the number of sources are derived, which minimizes source enumeration error probability. Simulation results show that our proposed approaches are superior to the conventional approaches from both the estimation accuracy and numerical detectability extent points of view. The results demonstrate that the second proposed approach has the feasibility to improve source enumeration performance if appropriate learning datasets are sufficiently provided.https://www.mdpi.com/2076-3417/11/4/1942electronic warfaresource enumerationeigenvalues of covariance matrixsubspace-based estimationuniform linear arraymachine learning |
| spellingShingle | Yunseong Lee Chanhong Park Taeyoung Kim Yeongyoon Choi Kiseon Kim Dongho Kim Myung-Sik Lee Dongkeun Lee Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation Applied Sciences electronic warfare source enumeration eigenvalues of covariance matrix subspace-based estimation uniform linear array machine learning |
| title | Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation |
| title_full | Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation |
| title_fullStr | Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation |
| title_full_unstemmed | Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation |
| title_short | Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation |
| title_sort | source enumeration approaches using eigenvalue gaps and machine learning based threshold for direction of arrival estimation |
| topic | electronic warfare source enumeration eigenvalues of covariance matrix subspace-based estimation uniform linear array machine learning |
| url | https://www.mdpi.com/2076-3417/11/4/1942 |
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