Pattern-Reconfigurable Sparse Linear Array Synthesis Under Minimum Element Spacing Control by Alternating Sequential Quadratic Programming

doi:10.1109/LAWP.2023.3240031

Pattern-Reconfigurable Sparse Linear Array Synthesis Under Minimum Element Spacing Control by Alternating Sequential Quadratic Programming

2023-06-01

2

PRE

AI

DOI：10.1109/LAWP.2023.3240031

Original

Original request for help

Save

Abstract

En 中文

A new method called alternating sequential quadratic programming is proposed to synthesize pattern-reconfigurable sparse linear arrays with minimum element spacing control. The method can find the common element positions and multiple sets of excitations for generating multiple reconfigurable patterns which accurately meet their given upper and lower pattern bounds. In addition, by introducing auxiliary weighting coefficients and collective excitation coefficient vectors and choosing them as optimization variables alternately, the proposed method can appropriately incorporate the minimum element spacing constraint into the pattern synthesis. Synthesized results show that the proposed method can give satisfactory reconfigurable pattern performance but save much more elements compared with some existing methods.

Keywords:

Optimization

Phased arrays

Shape

Quadratic programming

Linear antenna arrays

Indexes

Upper bound

Alternating sequential quadratic programming (ASQP)

minimum spacing constraint

pattern-reconfigurable array

pattern synthesis

sparse array

Pattern-Reconfigurable Sparse Linear Array Synthesis Under Minimum Element Spacing Control by Alternating Sequential Quadratic Programming

Abstract

Journal

Organization