Two 2-D DOA Estimation Methods with Full and Partial Generalized Virtual Aperture Extension Technology
We address the two-dimensional direction-of-arrival (2-D DOA) estimation problem for L-shaped uniform linear array (ULA) using two kinds of approaches represented by the subspace-like method and the sparse reconstruction method. Particular interest emphasizes on exploiting the generalized conjugate...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2019/3924569 |
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| author | Riheng Wu Yangyang Dong Zhenhai Zhang Le Xu |
| author_facet | Riheng Wu Yangyang Dong Zhenhai Zhang Le Xu |
| author_sort | Riheng Wu |
| collection | DOAJ |
| description | We address the two-dimensional direction-of-arrival (2-D DOA) estimation problem for L-shaped uniform linear array (ULA) using two kinds of approaches represented by the subspace-like method and the sparse reconstruction method. Particular interest emphasizes on exploiting the generalized conjugate symmetry property of L-shaped ULA to maximize the virtual array aperture for two kinds of approaches. The subspace-like method develops the rotational invariance property of the full virtual received data model by introducing two azimuths and two elevation selection matrices. As a consequence, the problem to estimate azimuths represented by an eigenvalue matrix can be first solved by applying the eigenvalue decomposition (EVD) to a known nonsingular matrix, and the angles pairing is automatically implemented via the associate eigenvector. For the sparse reconstruction method, first, we give a lemma to verify that the received data model is equivalent to its dictionary-based sparse representation under certain mild conditions, and the uniqueness of solutions is guaranteed by assuming azimuth and elevation indices to lie on different rows and columns of sparse signal cross-correlation matrix; we then derive two kinds of data models to reconstruct sparse 2-D DOA via M-FOCUSS with and without compressive sensing (CS) involvements; finally, the numerical simulations validate the proposed approaches outperform the existing methods at a low or moderate complexity cost. |
| format | Article |
| id | doaj-art-ef6222bf24574a77b85f10dc82c653d3 |
| institution | Kabale University |
| issn | 1687-5869 1687-5877 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-ef6222bf24574a77b85f10dc82c653d32025-02-03T06:08:31ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772019-01-01201910.1155/2019/39245693924569Two 2-D DOA Estimation Methods with Full and Partial Generalized Virtual Aperture Extension TechnologyRiheng Wu0Yangyang Dong1Zhenhai Zhang2Le Xu3The Department of Information Engineering, Wenjing College, Yantai University, Yantai 264005, ChinaThe School of Electrical Engineering, Xidian University, Xi’an 710071, ChinaThe School of Mechatronics Engineering, The Beijing Institute of Technology, Beijing 100081, ChinaThe College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, ChinaWe address the two-dimensional direction-of-arrival (2-D DOA) estimation problem for L-shaped uniform linear array (ULA) using two kinds of approaches represented by the subspace-like method and the sparse reconstruction method. Particular interest emphasizes on exploiting the generalized conjugate symmetry property of L-shaped ULA to maximize the virtual array aperture for two kinds of approaches. The subspace-like method develops the rotational invariance property of the full virtual received data model by introducing two azimuths and two elevation selection matrices. As a consequence, the problem to estimate azimuths represented by an eigenvalue matrix can be first solved by applying the eigenvalue decomposition (EVD) to a known nonsingular matrix, and the angles pairing is automatically implemented via the associate eigenvector. For the sparse reconstruction method, first, we give a lemma to verify that the received data model is equivalent to its dictionary-based sparse representation under certain mild conditions, and the uniqueness of solutions is guaranteed by assuming azimuth and elevation indices to lie on different rows and columns of sparse signal cross-correlation matrix; we then derive two kinds of data models to reconstruct sparse 2-D DOA via M-FOCUSS with and without compressive sensing (CS) involvements; finally, the numerical simulations validate the proposed approaches outperform the existing methods at a low or moderate complexity cost.http://dx.doi.org/10.1155/2019/3924569 |
| spellingShingle | Riheng Wu Yangyang Dong Zhenhai Zhang Le Xu Two 2-D DOA Estimation Methods with Full and Partial Generalized Virtual Aperture Extension Technology International Journal of Antennas and Propagation |
| title | Two 2-D DOA Estimation Methods with Full and Partial Generalized Virtual Aperture Extension Technology |
| title_full | Two 2-D DOA Estimation Methods with Full and Partial Generalized Virtual Aperture Extension Technology |
| title_fullStr | Two 2-D DOA Estimation Methods with Full and Partial Generalized Virtual Aperture Extension Technology |
| title_full_unstemmed | Two 2-D DOA Estimation Methods with Full and Partial Generalized Virtual Aperture Extension Technology |
| title_short | Two 2-D DOA Estimation Methods with Full and Partial Generalized Virtual Aperture Extension Technology |
| title_sort | two 2 d doa estimation methods with full and partial generalized virtual aperture extension technology |
| url | http://dx.doi.org/10.1155/2019/3924569 |
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