应用遗传算法和序列锥规划进行圆阵的方向图综合

Pattern synthesis of the circular array using genetic algorithm and sequential cone programming

为了解决圆阵方向图旁瓣电平较高的问题,提出了一种圆阵方向图综合方法。该方法将圆阵方向图的峰值旁瓣电平作为目标函数,首先采用改进遗传算法对阵元位置和阵元权值进行联合优化,不仅避免了算法的早熟收敛,而且符合理论意义上的全局最优。其次将遗传算法的优化结果作为初始迭代点,在其附近利用一阶泰勒级数将非凸的圆阵方向图综合问题转化为序列锥规划问题,以便采用凸优化理论进行高效求解。由于该算法增加了寻优操作的后期变异能力,因而有效提高了优化性能。最后仿真表明,阵元数一定的情况下,算法在进一步降低峰值旁瓣电平的同时,可有效减小其动态变化范围,使得圆阵方向图综合性能更优。

In order to solve the problem of the high side lobe level in the circular array,a pattern synthesis method is proposed.It firstly makes the peak side lobe level as its fitness function to optimize the locations and coefficients of the array elements based on genetic algorithm,which is modified in order to avoid premature convergence.It accords with the academic global optimization and can greatly improve the algorithm search performance.Secondly,it makes the optimal results in the first step as a good initial iterative point and adopts Taylor approximation to transform the original pattern synthesis problem which is non-convex into sequential cone programming framework near the good initial point.After this it can be solved efficiently by the convex optimization.Due to the late variability capacity in the operation,it can effectively improve the optimization performance.Lastly simulations demonstrate that when the array number is constant,this method can not only reduce the side lobe level but also minimize its dynamic range.And it achieves a better performance of circular array pattern in comparison with the reference method.