Complex-Weight Sparse Linear Array Synthesis by Bayesian Compressive Sampling
Complex-Weight Sparse Linear Array Synthesis by Bayesian Compressive Sampling
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DOI：
10.1109/tap.2012.2189742
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发表时间：
2012-05-01
影响因子：
5.7
通讯作者：
Massa, Andrea
中科院分区：
文献类型：
--
作者：
Oliveri, Giacomo;Carlin, Matteo;Massa, Andrea
提出了一种新的匹配任意参考方向图的最大稀疏线阵综合方法。在稀疏约束优化的框架下，该方法利用多任务贝叶斯压缩感知理论，使设计具有任意辐射和几何约束的复杂非厄米布局。通过铸造的模式匹配问题的概率公式，相关向量机技术被用作解决方案的工具。数值评估指出了[18]的扩展到复杂模式的拟议实现的进步，并且与最先进的稀疏阵列合成方法相比，它给出了该方法的可靠性，灵活性和数值效率的一些指示。
An innovative method for the synthesis of maximally sparse linear arrays matching arbitrary reference patterns is proposed. In the framework of sparseness constrained optimization, the approach exploits the multi-task Bayesian compressive sensing theory to enable the design of complex non-Hermitian layouts with arbitrary radiation and geometrical constraints. By casting the pattern matching problem into a probabilistic formulation, a Relevance-Vector-Machine technique is used as solution tool. The numerical assessment points out the advances of the proposed implementation over the extension to complex patterns of [18] and it gives some indications about the reliability, flexibility, and numerical efficiency of the approach also in comparison with state-of-the-art sparse-arrays synthesis methods.