Sidelobe reduction in array-pattern synthesis using genetic algorithm

A simple and flexible genetic algorithm (GA) for pattern synthesis of antenna array with arbitrary geometric configuration is presented. Unlike conventional GA using binary coding and binary crossover, this approach directly represents the array excitation weighting vectors as complex number chromosomes and uses decimal linear crossover without a crossover site. Compared with conventional GAs, this approach has a few advantages: giving a clearer and simpler representation of the problem, simplifying chromosome construction, and totally avoiding binary encoding and decoding so as to simplify software programming and to reduce CPU time. This method also allows us to impose constraints on phases and magnitudes of complex excitation coefficients for preferable implementation in practice using digital phase shifters and digital attenuators. Successful applications show that the approach can be used as a general tool for pattern synthesis of arbitrary arrays.

[1]  M. Shimizu.  Determining the excitation coefficients of an array using genetic algorithms , 1994, Proceedings of IEEE Antennas and Propagation Society International Symposium and URSI National Radio Science Meeting.

[2]  A. P. Anderson,et al.  Array pattern nulling by element position perturbations using a genetic algorithm , 1994 .

[3]  M. H. Er,et al.  Application of constrained optimization techniques to array pattern synthesis , 1993, Signal Process..

[4]  Randy L. Haupt,et al.  Thinned arrays using genetic algorithms , 1993, Proceedings of IEEE Antennas and Propagation Society International Symposium.

[5]  Y. Jiao,et al.  A new low-side-lobe pattern synthesis technique for conformal arrays , 1993 .

[6]  R.T. Compton,et al.  A numerical pattern synthesis algorithm for arrays , 1990, International Symposium on Antennas and Propagation Society, Merging Technologies for the 90's.

[7]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[8]  E. Dufort,et al.  Pattern synthesis based on adaptive array theory , 1989 .

[9]  C. Burrus,et al.  Array Signal Processing , 1989 .

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  Constantine A. Balanis,et al.  Antenna Theory: Analysis and Design , 1982 .