OPTIMAL SUB-ARRAYING OF COMPROMISE PLANAR ARRAYS THROUGH AN INNOVATIVE ACO-WEIGHTED PROCEDURE

In this paper, the synthesis of sub-arrayed monopulse planar arrays providing an optimal sum pattern and best compromise difference patterns is addressed by means of an innovative clustering approach based on the Ant Colony Optimizer. Exploiting the similarity properties of optimal and independent sum and difference excitation sets, the problem is reformulated into a combinatorial one where the definition of the sub-array configuration is obtained through the search of a path within a weighted graph. Such a weighting strategy allows one to effectively sample the solution space avoiding bias towards sub-optimal solutions. The sub-array weight coefficients are then determined in an optimal way by exploiting the convexity of the problem at hand by means of a convex programming procedure. Representative results are reported to assess the effectiveness of the weighted global optimization and its advantages over previous implementations.