Study of Noncoherent Sparse Subarrays for Direction Finding Based on Low-Rank and Sparse Recovery
<|reference_start|>Study of Noncoherent Sparse Subarrays for Direction Finding Based on Low-Rank and Sparse Recovery: 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<|reference_end|>
@article{leite2024study,
    title={Study of Noncoherent Sparse Subarrays for Direction Finding Based on
  Low-Rank and Sparse Recovery},
    author={W. Leite and R. C. de Lamare},
    journal={arXiv preprint arXiv:2402.15681},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15681},
    primaryClass={cs.IT eess.SP math.IT}
}