Antenna position optimization method based on adaptive genetic algorithm with self-supervised differential operator for distributed coherent aperture radar

The performance of the distributed coherent aperture radar (DCAR) is heavily influenced by the antenna positions. Therefore, an antenna position optimization method is proposed based on the adaptive genetic algorithm with a self-supervised differential operator. In the proposed method, the antenna positions are firstly coded as the chromosomes of the population with multiple constraints, and the reciprocal of the peak side lobe level (PSLL) of the beam pattern is calculated as the fitness function for optimization. Then, the adaptive probabilities are calculated for the crossover and mutation of chromosomes and a self-supervised differential operator is utilized in the mutation. Finally, the optimal antenna positions for DCAR can be obtained with the lowest PSLL compared with the existing methods. The effectiveness of the proposed method is verified by linear and planar DCARs, respectively.