In this paper, two different architectures based on fully and partially clustered arrays are proposed to optimize the array patterns. In the fully clustered arrays, all the elements of the original array were divided into several equal subarrays, while in the partially clustered arrays, only the side elements were grouped into subarrays, and the central elements were left individually. The second architecture enjoys many advantages compared to the first one. The proposed clustered arrays use quantized amplitude distributions, thus, their corresponding patterns were associated with high side lobes. To overcome this problem, a constraint mask was included in the pattern optimization process. Simulation results show that the peak sidelobe level and the complexity of the feeding network in the partially clustered arrays can be reduced to more than -28 dB and 70.833% respectively, for a total of 48 array elements, number of individual central elements = 24, number of clusters on both sides of the array Q = 4, and number of elements in each side cluster M=6. Finally, the principles of the proposed clustered arrays were extended and applied to the two dimensional planar arrays.
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