Yadav, Shekhar KumarShekhar KumarYadavGeorge, Nithin V.Nithin V.George2025-10-302025-10-302025-01-01[9798331531478]10.1109/VTC2025-Spring65109.2025.111742922-s2.0-105019050730http://repository.iitgn.ac.in/handle/IITG2025/33420In array signal processing, integer linear arrays with sensors placed at multiples of the half-wavelength are standard. In this work, we overcome aperture constraints that often lead to under-utilized sensor resources by using rational arrays that position sensors at rational locations. By formulating direction-of-arrival (DOA) estimation as a joint sparse support recovery (JSSR) problem, we demonstrate that rational arrays can achieve superior performance compared to conventional integer arrays. Notably, rational non-uniform arrays can resolve O(M²) uncorrelated sources using only M sensors, even when the available aperture is limited, a feat that integer non-uniform arrays like nested and coprime configurations cannot attain without large apertures. We derive the performance of these arrays using a sufficient JSSR condition and validate our findings through extensive simulations covering both overdetermined and underdetermined DOA estimation scenarios.falseAperture constraint | Difference coarray | Joint sparse support recovery | Rational arrays | Sparse Bayesian learningConference Paper20250