Optimization of subarray partition based on genetic algorithm

Suboptimum performance may be obtained by partially adaptive processing at subarray stage where the problem of how to group the subarrays is an important task. In this paper, an effective subarray partition method based on genetic algorithm is proposed, which divides a uniform linear array employing amplitude tapering into contiguous subarrays of unequally sized. In the genetic algorithm, the crossovers are prevented by including an appropriate penalty term in the cost function. The simulation results are given to show that the subarray configuration has few obvious grating lobes while its clutter suppression performance is close to that at element stage.