Abstract
A computationally effective hybrid approach to define the 'optimal' compromise between sum and difference patterns in monopulse arrays is presented. First, the partitioning into sub-arrays is performed by exploiting the knowledge of independently optimal sum and difference excitations. Then, the sub-array gains are computed by means of a gradient-based procedure, which takes advantage from the convexity of the problem at hand. Selected results are shown and compared with those from state-of-the-art methods in dealing with representative test cases.
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