电子学报 ›› 2016, Vol. 44 ›› Issue (9): 2168-2174.DOI: 10.3969/j.issn.0372-2112.2016.09.022

• 学术论文 • 上一篇    下一篇

基于多任务贝叶斯压缩感知的稀疏可重构天线阵的优化设计

沈海鸥, 王布宏, 李龙军   

  1. 空军工程大学信息与导航学院, 陕西西安 710077
  • 收稿日期:2014-12-24 修回日期:2015-04-21 出版日期:2016-09-25
    • 作者简介:
    • 沈海鸥 女,1990年7月出生于甘肃省兰州市,现为空军工程大学信息与导航学院博士研究生,主要研究方向为阵列信号处理和雷达信号处理.E-mail:haioushen1990@sina.com;王布宏 男,1975年12月出生于山西省太原市,现为空军工程大学教授、博士生导师,主要从事阵列信号处理、阵列校正等方面的研究工作.E-mail:wbhyl@aliyun.com
    • 基金资助:
    • 国家自然科学基金资助项目 (No.61172148)

Optimal Design of Sparse Reconfigurable Antenna Array Based on Multitask Bayesian Compressed Sensing

SHEN Hai-ou, WANG Bu-hong, LI Long-jun   

  1. School of Information and Navigation, Air Force Engineering University, Xi'an, Shaanxi 710077, China
  • Received:2014-12-24 Revised:2015-04-21 Online:2016-09-25 Published:2016-09-25
    • Supported by:
    • National Natural Science Foundation of China (No.61172148)

摘要:

建立方向图可重构天线的联合稀疏模型,基于多任务贝叶斯压缩感知理论提出一种稀疏可重构天线阵的优化设计方法.该方法在实现方向图精确重构的同时可以大幅减少天线数量,节省平台空间,降低设计成本.首先基于多任务贝叶斯压缩感知理论建立多目标方向图的稀疏优化模型,根据权值向量的先验概率分布,利用快速相关向量机估计超参数的最大后验概率来得到多组阵元位置及其激励,实时改变激励以获得不同方向图的稀疏逼近.仿真验证了该方法能够以较少的阵元个数和较高的方向图拟合精度快速实现方向图重构.

关键词: 稀疏布阵, 方向图可重构天线, 多任务贝叶斯压缩感知, 相关向量机

Abstract:

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.

Key words: sparse array, pattern reconfigurable antenna, multitask Bayesian compressed sensing, relevance vector machine

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