Optimally Dense Nonredundant Sparse Sensor Array Designs

Sparse sensor arrays have become increasingly popular in recent years for their ability to mitigate mutual coupling effects, enhance array aperture, and detect more signal sources compared with uniform arrays. Nonredundant array (NRA) is a popular sparse array type that has no redundancy in its difference co-array. In an optimally short NRA, also known as minimum hole array (MHA), the NRA length is minimized for a desired number of array sensors. In contrast to certain arrays, the geometry of the optimally short NRA is typically determined using search techniques because it does not have a closed-form mathematical representation. In this article, our focus is on the design of optimally dense NRAs (ODNRAs). In an ODNRA the number of array sensors is maximized for a desired array length. We define optimization problems to obtain four types of these arrays. These four types are: ODNRA, ODNRA with reduced mutual coupling (ODNRArmc), ODNRA with desired number of elements (ODNRAdn), and ODNRA with reduced mutual coupling and desired number of elements (ODNRArmcdn). Performance studies and simulation results show that the proposed arrays outperform many state-of-the-art sparse arrays in terms of direction of arrival (DOA) estimation accuracy, robustness against mutual coupling between array elements, and ability in resolving closely spaced signal sources.