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

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.

Automated summary

A new method called alternating sequential quadratic programming is proposed for synthesizing pattern-reconfigurable sparse linear arrays. The method can find common element positions and multiple sets of excitations, generating reconfigurable patterns that meet given upper and lower pattern bounds. By incorporating the minimum element spacing constraint, the method saves more elements while maintaining satisfactory performance.