基于多约束凸优化的重叠子阵加权方法

Overlapped Subarray Weighting Method Based on Multiple Constrained Convex Optimization

为了克服阵列雷达重叠子阵在有限视场扫描时的高副瓣问题,需要对子阵进行加权以形成宽主瓣低副瓣的子阵因子方向图。针对此问题,文中对子阵阵因子的主瓣和副瓣区域进行了精细划分,并在主副瓣区域上对阵因子方向图进行约束;引入了零陷角度和深度的约束更好地限制了第一零点的位置与电平;结合凸优化求解方法实现了平顶子阵阵因子优化。仿真结果表明,所提方法能够在满足方向图扫描范围的前提下有效降低扫描时出现的高副瓣,与传统群体智能算法相比,该方法能保证整个扫描角度上电平的一致性。

In order to solve the problem of high sidelobe in finite field scanning of overlapped subarrays in array radar,it is necessary to broaden main lobe and lower side lobe of subarray pattern by weighting the subarrays.As for this problem,main lobe and side lobe region are partitioned and the level are restrained subtly at first.Secondly,The positions and level of the first null are limited by the constraints of null angle and depth.Finally,the optimal flat-top subarray factor is obtained by convex optimization method.The simulation results show that this method can suppress the side lobe level efficiently in the condition of satisfying the range of beam scanning.Compared with the conventional swarm intelligence algorithm,this method can guarantee lower sidelobe level in the whole scanning range.