Deterministic Sparse Array Capon Beamformer Design for Mitigating Spatially-Closed Interferences

Sparse arrays have attracted increased attention due to their capability of striking the best compromise between performance and complexity. High spatial resolution associated with a large aperture makes sparse arrays most effective in combating spatially closed interferences, which are notoriously deteriorative. The configuration of sparse arrays achieving the maximum array gain generally varies with changing scenario, which is referred to as reconfigurable sparse arrays. On the contrary, deterministic sparse arrays have a fixed structure, thus more preferable in practical applications due to its hardware efficiency, although not optimal in terms of adaptive interference mitigation. Thus, it is preferred that deterministic sparse arrays could be designed for mitigating spatially closed interferences. In this work, we first eliminate the dependence of the array gain on array configurations utilizing the relationship between the spatial correlation coefficient (SCC) and array gain, in turn transforming the problem of reconfigurable arrays to that of deterministic arrays. We then propose a modified Alternative Direction Method of Multipliers (ADMM) algorithm to solve the sparse array design problem. We also compare the modified ADMM algorithm with our previously proposed Difference of Convex Sets (DCS) method, and results show that the modified ADMM is insensitive against initial search points and outperforms the previously proposed DCS method.