基于辅助相位优化的交替投影阵列综合算法
发布时间：2018-11-22 20:36
【摘要】：本文提出一种新的方向图快速综合算法.该方法基于交替投影算法(AP),首先对目标方向图幅度进行限制,其次引入了目标方向图相位变量并对其进行优化,得到最佳目标方向图相位,最后结合临近分裂方法来求解有约束的最小二乘问题(CLMS)以获得满足要求的激励.相比于其他AP类算法只对方向图幅度进行限制的处理,本文对方向图相位的优化处理有助于提高算法的收敛速度和性能.实验结果表明,无论有无激励约束的情况下,该算法均能取得良好的优化效果,且适用于任意阵列,具有很好的推广能力.
[Abstract]:In this paper, a new fast pattern synthesis algorithm is proposed. Based on the alternating projection algorithm (AP), the amplitude of the target direction map is firstly restricted, and then the phase variable of the target direction map is introduced and optimized to obtain the optimal target direction map phase. Finally, the constrained least squares problem (CLMS) is solved by using the near splitting method to obtain the required excitation. Compared with other AP algorithms, the phase optimization is helpful to improve the convergence speed and performance of the algorithm. The experimental results show that the proposed algorithm can achieve good optimization results with or without excitation constraints, and is suitable for arbitrary arrays and has a good generalization ability.
【作者单位】： 南京理工大学电子工程与光电技术学院;
【基金】：国家自然科学基金(No.11273017,No.61471196) 江苏省普通高校学术学位研究生科研新计划项目(No.KYLX16-0448)
【分类号】：TN820
，
本文编号：2350520
[Abstract]:In this paper, a new fast pattern synthesis algorithm is proposed. Based on the alternating projection algorithm (AP), the amplitude of the target direction map is firstly restricted, and then the phase variable of the target direction map is introduced and optimized to obtain the optimal target direction map phase. Finally, the constrained least squares problem (CLMS) is solved by using the near splitting method to obtain the required excitation. Compared with other AP algorithms, the phase optimization is helpful to improve the convergence speed and performance of the algorithm. The experimental results show that the proposed algorithm can achieve good optimization results with or without excitation constraints, and is suitable for arbitrary arrays and has a good generalization ability.
【作者单位】： 南京理工大学电子工程与光电技术学院;
【基金】：国家自然科学基金(No.11273017,No.61471196) 江苏省普通高校学术学位研究生科研新计划项目(No.KYLX16-0448)
【分类号】：TN820
，
本文编号：2350520
本文链接：https://www.wllwen.com/kejilunwen/xinxigongchenglunwen/2350520.html
