Subarray weighting method for sidelobe suppression of difference pattern based on genetic algorithm

This paper studies the method for sidelobe suppression of difference pattern at subarray level in phased array. Sidelobe suppression of difference pattern by digital weighting at subarray level takes advantage that the hardware cost and complexity can be reduced effectively. The optimization method for subarray weights based on genetic algorithm is presented. In order to avoid being constrained on local optimal solution of genetic operation, we partition the genetic optimization process into two stages. Bayliss weight is adopted as objective function for the first stage and parameters of pattern for the second one. Compared with conventional GA, the improved GA achieves better sidelobe suppression performance. The simulation results verify the validity of the proposed method.