@techreport{elediasc12888,
            year = {2024},
       publisher = {ELEDIA Research Center - University of Trento},
            type = {Technical Report},
          author = {Arianna BENONI and Paolo ROCCA and Nicola ANSELMI and Andrea MASSA},
           title = {Hilbert-Based Clustering Approach for Linear Arrays},
           month = {June},
             url = {http://www.eledia.org/students-reports/888/},
        keywords = {Local Optimization, Array Synthesis, Sub-Arraying},
        abstract = {This research examines the clustering of linear phased arrays (PAs)
incorporating complex weights. Through exploitation of the intrinsic
locality-preservation property associated with the Hilbert curve, the problem's
dimensionality is decreased. Subsequently, a basic clustering algorithm
is employed to optimize the alignment of the radiated pattern with a
predetermined reference. We systematically assess both contiguous and
noncontiguous partitions of the Hilbert-ordered list of complex excitations
to comprehensively sample the solution space. Representative outcomes,
encompassing reference PAs generating steered pencil and shaped beams, are
provided for validation and to underscore the efficacy of our methodology
relative to state-of-the-art k-means algorithms.}
}