Sparse optimization of planar radio antenna arrays using a genetic algorithm

Radio antenna arrays have many advantages for astronomical observations, such as high resolution, high sensitivity, multi-target simultaneous observation, and flexible beam formation. Problems surrounding key indices, such as sensitivity enhancement, scanning range extension, and sidelobe level suppression, need to be solved urgently. Here, we propose a sparse optimization scheme based on a genetic algorithm for a 64-array element planar radio antenna array. As optimization targets for the iterative process of the genetic algorithm, we use the maximum sidelobe levels and beamwidth of multiple cross-section patterns that pass through the main beam in three-dimensions, with the maximum sidelobe levels of the patterns at several different scanning angles. Element positions are adjusted for iterations, to select the optimal array configuration. Following sparse layout optimization, the simulated 64-element planar radio antenna array shows that the maximum sidelobe level decreases by 1.79 dB, and the beamwidth narrows by 3°. Within the scan range of ±30°, after sparse array optimization, all sidelobe levels decrease, and all beamwidths narrow. This performance improvement can potentially enhance the sensitivity and spatial resolution of radio telescope systems.