大孔径超稀疏阵列综合算法研究与应用

超稀疏阵列大孔径场景下最优布阵求解问题是在满足特定副瓣电平要求下，通过对布阵位置和阵元权值的优化获得最稀疏解。该问题属于NP-HARD问题，求解时存在搜索空间大、搜索时间长以及难以求得全局最优解等问题。为了解决上述问题，本文基于交替寻优的思想提出了一种针对大孔径场景下超稀疏阵列方向图综合的快速算法。该算法通过对求解空间进行划分后交替优化快速得出一个初始解，在初始解基础上构建相邻域形成新的求解空间，通过二次搜索寻优得到最优解。本算法通过对初始解空间进行划分和相邻域的构建，可大幅缩短每次优化的时间。本文通过对超稀疏线阵若干场景进行了仿真验证，仿真结果证明了该算法的有效性。

Research and Application of Synthesis Algorithm for Large Aperture Sparse Array

The optimization of large aperture sparse array is to find out the optimal position and weight of array elements subject to specific condition of side lobe level, which belongs to the NP-hard problem. Normally, it will consume massive computational cost to find out the global solution as the scale of solution space is extremely large. To overcome the problem, a new method based on alternative direction multiplier is proposed in this paper. Firstly, an initial solution is found out by dividing the solution space into several subspaces and optimizing the problem alternatively. Then, a new solution space is built based on the neighborhood of previous solution, which is used for the next iteration until the algorithm converges. Our new algorithm could reduce amounts of time by dividing the solution space and rebuilding the neighborhood properly in each iteration. Several application scenarios of large aperture sparse array are simulated, and the results prove the effectiveness of our algorithm.