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Maximally Sparse Steerable Array Antennas Via Convex Optimizations
Achieving the minimum number of radiating elements in the Active Electronically Scanned Array (AESA) synthesis represents a key problem in those applications where the power consumption, the weight, and the hardware/software complexity of the radiating system have a strong impact on the system cost. A first approach to the problem implements a method based on the sequential convex minimizations showing that the number of antenna elements in the array can be efficiently reduced by casting the pattern synthesis problem into the compressive sensing (CS) framework of cardinality constrained optimization and solving with the reweighted ℓ1-norm minimization algorithm. Another approach for solving cardinality constrained problems is the fast branch-and-bound, which, unlike the first approach, provides a parallel solution for a set of convex problems. In this paper we propose both techniques for the synthesis of planar, non super-directive, maximally sparse, steerable arrays. The proposed synthesis scheme optimizes simultaneously the weight coefficients and sensor positions of a planar array that radiates a steerable pencil beam pattern, satisfying a prescribed power mask and avoiding to constraint the fitting of any a priori assigned reference field pattern. Numerical tests are presented to show the high efficiency in achieving the desired steerable radiation pattern with the minimum number of antenna elements.
D’Urso Michele, Prisco Giancarlo, Tumolo Roberto Michele
Paper for Specialistic Magazine
IEEE Transactions on Antennas and Propagation
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