Study of Noncoherent Sparse Subarrays for Direction Finding Based on Low-Rank and Sparse Recovery
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929256242610176 |
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| author | Leite, W. de Lamare, R. C. |
| author_facet | Leite, W. de Lamare, R. C. |
| contents | This paper investigates the problem of noncoherent direction-of-arrival (DOA) estimation using different sparse subarrays. In particular, we present a Multiple Measurements Vector (MMV) model for noncoherent DOA estimation based on a low-rank and sparse recovery optimization problem. Moreover, we develop two different practical strategies to obtain sparse arrays and subarrays: i) the subarrays are generated from a main sparse array geometry (Type-I sparse array), and ii) the sparse subarrays that are directly designed and grouped together to generate the whole sparse array (Type-II sparse array). Numerical results demonstrate that the proposed MMV model can benefit from multiple data records and that Type-II sparse noncoherent arrays are superior in performance for DOA estimation |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15681 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Study of Noncoherent Sparse Subarrays for Direction Finding Based on Low-Rank and Sparse Recovery Leite, W. de Lamare, R. C. Information Theory Signal Processing This paper investigates the problem of noncoherent direction-of-arrival (DOA) estimation using different sparse subarrays. In particular, we present a Multiple Measurements Vector (MMV) model for noncoherent DOA estimation based on a low-rank and sparse recovery optimization problem. Moreover, we develop two different practical strategies to obtain sparse arrays and subarrays: i) the subarrays are generated from a main sparse array geometry (Type-I sparse array), and ii) the sparse subarrays that are directly designed and grouped together to generate the whole sparse array (Type-II sparse array). Numerical results demonstrate that the proposed MMV model can benefit from multiple data records and that Type-II sparse noncoherent arrays are superior in performance for DOA estimation |
| title | Study of Noncoherent Sparse Subarrays for Direction Finding Based on Low-Rank and Sparse Recovery |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2402.15681 |