Research on Multi-Constrained Genetic Algorithm for Distributed Radar Elements Position Optimization

Distributed radar was a new type of radar that raised wide concern at home and abroad in recent years. It had good maneuverability, low cost and high reliability. However, due to the sparse arrangement of distributed radar elements, grating lobes and high side lobes problems were easy to occur. It badly influenced the performance of the array. In order to optimize the beam pattern of the distributed array radar, a method based on multi-constrained genetic algorithm was proposed. In the proposed method, firstly real valued mapping coding of chromosome with minimum element spacing constraint was used and the population was initialed. Then a modified election operator which augments and then abates the population was applied to the individuals of the next iteration. Next, the broad sense crossover and mutation operator with adaptive parameters were utilized. The method can not only effectively suppress the system grating lobes, but also significantly reduce the system side-lobe level with multiple constraints. Compared with the existing methods, the method proposed is simple and easy, and has stronger global search ability. The simulation results demonstrate the effectiveness and robustness of the proposed algorithm.