分布式雷达节点位置优化的多约束遗传算法研究

分布式雷达是近年来国内外广泛关注的一种新体制雷达，具有机动性强、成本低、可靠性高等优点。但是由于分布式雷达节点的稀疏布置，容易产生栅瓣、高旁瓣等问题，严重影响雷达系统性能。本文基于多约束遗传算法提出了一种分布式雷达节点位置优化方法。该方法首先基于约束最小间距对节点位置进行实值映射编码，并对遗传算法的种群进行初始化，然后对种群进行先增广后收缩的选择处理，再根据自适应概率进行交叉和变异处理，最后经迭代实现分布式雷达节点位置的优化设计。该方法不仅能够有效抑制系统栅瓣，还能够满足多约束需求显著降低系统旁瓣电平。与已有方法相比，该方法简单易行、全局搜索能力强。通过仿真验证了所提方法的有效性和稳健性。 

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.