基于增广矩阵束方法的平面天线阵列综合

针对平面阵列的稀布优化问题，提出了一种基于增广矩阵束方法的减少阵元数目、求解阵元位置和设计幅度激励的优化方法。首先对期望平面阵的方向图进行采样并由采样点数据构造增广矩阵，对此矩阵进行奇异值（SVD）分解，确定在误差允许范围内所需的最小阵元数目；然后基于广义特征值分解分别计算两组特征值，并根据类ESPRIT算法对特征值进行配对；最后在最小二乘准则条件下根据正确的特征值对求解平面阵列的阵元位置和激励。仿真结果表明该算法具有较高的计算效率和数值精度。...

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Bibliographic Details
Published in电子技术应用 Vol. 38; no. 12; pp. 101 - 104
Main Author 郑美燕 陈客松
Format Journal Article
LanguageChinese
Published 电子科技大学 电子工程学院,四川 成都,611731 2012
Subjects
低秩逼近矩阵
增广矩阵束方法（MEMP）
奇异值分解（SVD）
平面阵列
稀布阵
平面阵列
低秩逼近矩阵
增广矩阵束方法(MEMP)
奇异值分解(SVD)
稀布阵
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Summary:针对平面阵列的稀布优化问题，提出了一种基于增广矩阵束方法的减少阵元数目、求解阵元位置和设计幅度激励的优化方法。首先对期望平面阵的方向图进行采样并由采样点数据构造增广矩阵，对此矩阵进行奇异值（SVD）分解，确定在误差允许范围内所需的最小阵元数目；然后基于广义特征值分解分别计算两组特征值，并根据类ESPRIT算法对特征值进行配对；最后在最小二乘准则条件下根据正确的特征值对求解平面阵列的阵元位置和激励。仿真结果表明该算法具有较高的计算效率和数值精度。
Bibliography:Zheng Meiyan, Chen Kesong （School of Electronic Engineering, UESTC, Chengdu 611731, China）
plannar array; sparse array; matrix enhanced and matrix pencil（MEMP）; singular value decomposition（SVD）; low rank approximation
For the optimization of sparse planar array, a new method based on matrix enhancement and matrix pencil is proposed to reduce the number of elements, to solve the elements＇ positions and to design the excitations. Firstly, an enhanced matrix is buih using the sampling data of the desired plannar array radiation pattern and the singular value decomposition （SVD） can be performed. Then the minimun number of elements can be determined. Secondly, the generalized eigen-deeomposition is employed to calculate the eigenvalues and the ESPRIT is utilized to pair the eigenvalues of each column. Finally,excitations and locations are calculated according to the correct pairing of eigenvalues. Simulation results are presented to demonstrate the efficiency and satisfactory accuracy of the proposed method.
11-230
ISSN:0258-7998