Abstract
Array synthesis under practical constraints is a vital design task.
Traditional array synthesis methods usually deal with isolated antenna
elements without considering mutual coupling (MC) or mounting-platform
effects, which results in unacceptable degradation in practical array
designs. An efficient machine learning-assisted array synthesis (MLAAS)
method is introduced using efficient active base element modeling
(ABEM). This method greatly extends the boundaries of practical antenna
array synthesis from the perspectives of both accuracy and design
freedom. Using much fewer samples than those in conventional MLAAS
methods, all possible element designs are accurately modeled into one
active base element (ABE). Compared with conventional active element
pattern (AEP)-based methods, the ABEM aims to predict AEPs for elements
with arbitrary allocations and electromagnetic (EM) surroundings,
therefore offering more degrees of freedom for practical array designs.
Four array design examples are used to verify the effectiveness of the
proposed method.