Optimal design of sparse reconfigurable antenna array based on multitask bayesian compressed sensing

被引：0
|
作者：
Shen H.-O. [1 ]
Wang B.-H. [1 ]
Li L.-J. [1 ]
机构：
[1] School of Information and Navigation, Air Force Engineering University, Xi'an, 710077, Shaanxi
来源：
| 2016年 / Chinese Institute of Electronics卷 / 44期
关键词：
Multitask Bayesian compressed sensing; Pattern reconfigurable antenna; Relevance vector machine; Sparse array;
D O I：
10.3969/j.issn.0372-2112.2016.09.022
中图分类号：
学科分类号：
摘要：
In light of the equivalent joint sparse learning model, an effective method based on multitask Bayesian compressed sensing (MT-BCS) is presented for the design of pattern reconfigurable antenna arrays. The method can dynamically reconfigure arbitrary radiation patterns with the exact pattern details and as fewer number of antenna elements as possible. Firstly, the sparse learning model of multiple reference patterns is built based on MT-BCS theory and priori assumption about the priori probability of weight vectors. Then fast relevance vector machine (RVM) is exploited to estimate maximum posterior probability of hyper-parameter and further to obtain array optimizing positions and excitations. By varying excitations and optimized element positions, different patterns with desired and precise particulars can be achieved. Simulation results validate the efficiency of the proposed method for the design of maximally sparse reconfigurable antenna. © 2016, Chinese Institute of Electronics. All right reserved.
引用
收藏
页码：2168 / 2174
页数：6
相关论文
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