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arXiv · 2402.15681

Study of Noncoherent Sparse Subarrays for Direction Finding Based on Low-Rank and Sparse Recovery

W. Leite·R. C. de Lamare
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Abstract

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

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W. Leite, R. C. de Lamare. 2024-02-24. Study of Noncoherent Sparse Subarrays for Direction Finding Based on Low-Rank and Sparse Recovery. https://arxiv.org/abs/2402.15681

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