TY - JOUR
T1 - A stochastic study of large arrays related to the number of electrically large aperture radiators
AU - Kaifas, Theodoros N.
AU - Babas, Dimitrios G.
AU - Miaris, George S.
AU - Vafiadis, Elias E.
AU - Siakavara, Katherine
AU - Toso, Giovanni
AU - Sahalos, John N.
PY - 2014
Y1 - 2014
N2 - A study of the maximally sparse large planar arrays with electrically large elements is presented. The conditional probabilities of the element placements and their resulting auxiliary radiation integrals are derived. Through them the average and the directivity pattern formulas are also derived. Employing these formulas, we present a convex method that provides a solution to the maximally sparse problem when main lobe constraints are imposed on the directivity pattern. In particular, by taking the possible types of elements into account, we manage to obtain the lower bound of the directivity that an array should exhibit. This lower bound is analytically derived in the form of a Pareto (sub-) set that classifies the possible arrays into feasible and nonfeasible ones. This Pareto set can also enable tradeoff studies to be conducted without the need to consider the full range of every parameter. From the procedure, several acceptable combinations of elements are obtained. Simulation results, which confirm the methodology, are presented.
AB - A study of the maximally sparse large planar arrays with electrically large elements is presented. The conditional probabilities of the element placements and their resulting auxiliary radiation integrals are derived. Through them the average and the directivity pattern formulas are also derived. Employing these formulas, we present a convex method that provides a solution to the maximally sparse problem when main lobe constraints are imposed on the directivity pattern. In particular, by taking the possible types of elements into account, we manage to obtain the lower bound of the directivity that an array should exhibit. This lower bound is analytically derived in the form of a Pareto (sub-) set that classifies the possible arrays into feasible and nonfeasible ones. This Pareto set can also enable tradeoff studies to be conducted without the need to consider the full range of every parameter. From the procedure, several acceptable combinations of elements are obtained. Simulation results, which confirm the methodology, are presented.
KW - Aperiodic arrays
KW - maximally sparse arrays
UR - http://www.scopus.com/inward/record.url?scp=84904177712&partnerID=8YFLogxK
U2 - 10.1109/TAP.2014.2316285
DO - 10.1109/TAP.2014.2316285
M3 - Article
AN - SCOPUS:84904177712
SN - 0018-926X
VL - 62
SP - 3520
EP - 3533
JO - IEEE Transactions on Antennas and Propagation
JF - IEEE Transactions on Antennas and Propagation
IS - 7
M1 - 6784335
ER -