  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
