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阵列置零的同时,阵列的旁瓣电平升高、主瓣增益降低甚至阵列指向都会发生改变,导致阵列性能降低。针对阵列置零时阵列性能降低问题,提出一种约束优化模型。在约束优化模型中不仅设置了零陷深度约束和近旁瓣电平约束,还设置了阵列期望方向增益约束及阵列指向约束。在满足约束条件下,使得阵列旁瓣电平最低。并且针对标准约束差分进化算法收敛慢,采用自适应约束差分进化(ε-SADE)算法,该算法采用多种变异方式相结合、自适应地调节交叉概率和缩放因子。运用自适应约束差分进化分别通过调节阵元相位和阵列功率一定时的阵元权值求解这个约束优化问题,仿真结果表明提出的方法实现了需求的目标方向图,利用自适应约束差分进化算法优化实现阵列置零是有效可行的。
At the same time as the array is set to zero, the sidelobe level of the array is increased, the gain of the main lobe is decreased, and even the orientation of the array is changed, which leads to the decrease of the array performance. Aiming at the problem of array performance reduction when the array is set to zero, a constrained optimization model is proposed. In the constrained optimization model, not only the nulling depth constraint and the near-side-lobe level constraint but also the desired directional gain constraint of the array and the array pointing constraint are set. Under the constraint condition, the sidelobe level of the array is the lowest. In addition, the standard constrained differential evolution algorithm converges slowly, using adaptive constrained differential evolution (ε-SADE) algorithm, which uses a combination of variations to adaptively adjust the crossover probability and scaling factor. The constrained optimization problem is solved by adaptively constrained differential evolution by adjusting the array element weights and the array power at a fixed time respectively. The simulation results show that the proposed method realizes the desired target pattern, and uses the adaptive constrained differential evolution algorithm to optimize Zeroing the array is effective and feasible.