Sparse array optimization for wideband signals using adaptive genetic algorithm

With the rapid increase of signal spectrum width, wideband spectrum sensing and signals detection face significant challenges, where wideband array technology plays an important role in system design and signal processing. To address the issues of reduced main lobe gain and increased side lobes in the beamforming pattern of wideband antenna arrays, an optimization method for sparse array antennas is proposed in this paper. An adaptive genetic algorithm-based wideband array optimization method is proposed to optimize the sparse array antennas efficiently. Compared with traditional genetic algorithm, this method minimizes peak sidelobe level and main lobe width as constraint, updates crossover and mutation probabilities, avoids getting stuck in local optima, and adaptively optimizes element positions. The optimized array configuration aims to ensure that the main lobe width is minimized while minimizing the peak sidelobe level. Simulation results show that, compared to the optimized array by conventional genetic algorithm, the optimized array configuration reduces the peak sidelobe level by about 1dB in both high and low frequency bands of wideband signals, while maintaining the main lobe half power beamwidth basically unchanged.