Synthesis of Non-separable Sparse Planar Array via Compressed Sensing

In this paper, an effective method is proposed for synthesizing non-separable sparse planar array to match the desired radiation pattern using as few elements as possible. The original synthesis is formulated as a sparse signal recovery convex problem based on Compressed Sensing (CS) theory by sampling on the reference 3-D pattern along with discretizing the 2-D aperture. In this way, the proposed method has the capability of achieving a complete optimization on the number of elements, the element weights as well as the element positions. Numerical experiment for matching non-separable Chebyshev pattern will demonstrate the effectiveness and sparseness of the proposed method.