Synthesis of sparse linear array with multiple patterns based on joint sparse recovery
Synthesis of sparse linear array with multiple patterns based on joint sparse recovery
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DOI：
10.1109/apusncursinrsm.2017.8072255
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Xiaowen Zhao;Qingshan Yang;Yunhua Zhang
中科院分区：
文献类型：
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作者：
Xiaowen Zhao;Qingshan Yang;Yunhua Zhang
提出了一种基于联合稀疏恢复的创新方法，用于稀疏线性阵列合成，使用最少数量的元素生成具有不同形状的多个图案。就压缩感知中的联合稀疏恢复而言，该综合被表述为具有混合 l2/l1 范数最小化的凸问题。然后适当地采用凸优化来有效地解决上述问题。所提出的方法可以同时对所有所需图案的元件数量、公共元件位置以及单个元件激励进行完整的优化。初步结果表明该方法在实现稀疏线性阵列（包括聚焦波束、余秒平方和平顶波束）方面的有效性和灵活性。
An innovative methodology based on joint sparse recovery is proposed for sparse linear array synthesis using a minimum number of elements to generate multiple patterns with different shapes. The synthesis is formulated as a convex problem with the minimization of mixed l2/l1-norm in terms of the joint sparse recovery in Compressed Sensing. Then convex optimization is properly adopted to solve the above problem efficiently. The proposed method can perform a complete optimization on the number of elements, the common element positions as well as the individual element excitations for all desired patterns simultaneously. Preliminary results are presented to show the effectiveness and flexibility of the proposed method in achieving a sparse linear array including focused, cosec-squared and flat-topped beams.