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

doi:10.3969/j.issn.0372-2112.2016.09.022

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

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

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

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/Abstract

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

中图分类号:

TN820

沈海鸥, 王布宏, 李龙军. 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[J]. 电子学报, 2016, 44(9): 2168-2174.

SHEN Hai-ou, WANG Bu-hong, LI Long-jun . Optimal Design of Sparse Reconfigurable Antenna Array Based on Multitask Bayesian Compressed Sensing[J]. Acta Electronica Sinica, 2016, 44(9): 2168-2174.

参考文献

[1] Haupt R L,Lanagan M.Reconfigurable antennas[J].IEEE Antennas and Propagation Magazine,2013,55(1):49-61.
[2] Kang W,Kim K.A radiation pattern-reconfigurable antenna for wireless communications[A].2011 IEEE International Symposium on Antennas and Propagation[C].Spokane,USA:IEEE Antennas and Propagation Society,2011.1545-1548.
[3] Wang B Z,Xiao S Q,Bai Y Y,Zhang G M.Researches on pattern reconfigurable antenna and its application in phase array[A].Proceeding of the 2011 International Workshop on Antenna Technology[C].HongKong,China:Agilent Technologies,2011.46-49.
[4] Kang W S,Park J A,Yoon Y J.Simple reconfigurable antenna with antenna with radiation pattern[J].IET Electronics Letters,2008,44(1):182-183.
[5] Rodriguez J A,Trastoy J C,Bregains J C,Ares F,Franceschetti G.Beam reconfiguration of linear arrays using parasitic elements[J].Electronics Letters,2006,42(3):131-133.
[6] Ding X,Wang B Z.A millimeter-wave pattern-reconfigurable antenna with a reconfigurable feeding network[J].Journal of Electromagnetic Waves and Applications,2013,27(5):649-658.
[7] Manica L,Rocca P,Martini A,Massa A.An innovative approach based on a tree-searching algorithm for the optimal matching of independently optimum sum and difference excitations[J].IEEE Transactions on Antenna and Propagation,2008,56(1):58-66.
[8] Folgueiras M A,Gonzalez A R,Pena F A.Optimal compromise among sum and difference patterns in monopulse antennas:use of subarrays and distributions with common aperture tail[J].Journal of Electromagnetic Waves and Applications,2009,23(17-18):2301-2311.
[9] Vescovo R.Reconfigurability and beam scanning with phase-only control for antenna arrays[J].IEEE Transactions on Antenna and Propagation,2008,56(6):1555-1565.
[10] Isernia T,Massa A,Morabito A F,Rocca P.On the optimal synthesis of phase-only reconfigurable antenna arrays[A].Proceedings of the 5th Conference on Antennas and Propagation (EuCAP)[C].Rome,Italy:IEEE Antennas and Propagation Society,2011.2074-2077.
[11] Morabito A F,Massa A,Rocca P,Isernia T.An effective approach to the synthesis of phase-only reconfigurable linear arrays[J].IEEE Transactions on Antenna and Propagation,2012,60(8):3622-3631.
[12] Mahanti G K,Chakraborty A,Das S.Phase-only and amplitude-phase-only synthesis of dual-beam pattern linear antenna arrays using floating-point genetic algorithms[J].Progress in Electromagnetics Research,2007,68:247-259.
[13] 侯青松,郭英,王布宏,王永良.基于格理论的非均匀稀疏线阵旁瓣结构的分析方法[J].电子学报,2010,38(6):1459-1463. Hou Qing-song,Guo Ying,Wang Bu-hong,Wang Yong-liang.Analysis of side lobe structure of nonuniform sparse linear array based on lattice theory[J].Acta Electronica Sinica,2010,38(6):1459-1463.(in Chinese)
[14] Oliveri G,Donelli M,Massa A.Linear array thinning exploiting almost difference sets[J].IEEE Transactions on Antenna and Propagation,2009,57(12):3800-3812.
[15] Sartori D,Oliveri G,Manica L,Massa A.Hybrid design of non-regular linear arrays with accurate control of the pattern sidelobes[J].IEEE Transactions on Antenna and Propagation,2013,61(12):6237-6242.
[16] Ji S,Xue Y,Carin L.Bayesian compressive sensing[J].IEEE Transactions on Signal Processing,2008,56(6):2346-2356.
[17] 季云云,杨震.脉冲噪声环境下高斯稀疏信源贝叶斯压缩感知重构[J].电子学报,2013,41(2):363-370. Ji Yun-yun,Yang Zhen.Bayesian compressed sensing for Gaussian sparse signals in the presence of impulsive noise[J].Acta Electronica Sinica,2013,41(2):363-370.(in Chinese)
[18] Oliveri G,Massa A.Bayesian compressive sampling for pattern synthesis with maximally sparse non-uniform linear arrays[J].IEEE Transactions on Antenna and Propagation,2011,59(2):467-481.
[19] Zhang W,Li L,Li F.Reducing the number of elements in linear and plannar antenna arrays with sparseness constrained optimization[J].IEEE Transactions on Antenna and Propagation,2011,59(8):3106-3111.
[20] Liu Y,Nie Z P,Liu Q H.Reducing the number of elements in a linear antenna array by the matrix pencil method[J].IEEE Transactions on Antenna and Propagation,2008,56(9):2955-2962.
[21] Liu Y,Liu Q H,Nie Z P.Reducing the number of elements in the synthesis of shaped-beam patterns by the forward-backward matrix pencil method[J].IEEE Transactions on Antenna and Propagation,2010,58(2):604-608.
[22] 杨鹏,闫飞,张胜辉,杨峰.基于FOCUSS算法的稀疏阵列综合[J].电子科技大学学报,2014,43(2):203-206. Yang Peng,Yan Fei,Zhang Sheng-hui,Yang-feng.Sparse array synthesis based on FOCUSS algorithm[J].Journal of University of Electronic Science and technology of China,2014,43(2):203-206.(in Chinese)
[23] Baron D,Duarte M F,Wakin M B,Sarvotham S,Baraniuk R G.Distributed Compressive Sensing[R].USA:Department of Electrical Engineering,Technion,2009.1-43.
[24] Sundman D,Chatterjee S,Skoglund M.A greedy pursuit algorithm for distributed compressive sensing[A].International Conference on Acoustics,Speech,and Signal Processing (ICASSP)[C].Kyoto,Japan:Inst Electr Electron Eng Signal Process Soc,2012.2729-2732.
[25] Ji S,Dunson D,Carin L.Multitask compressive sensing[J].IEEE Transactions on Signal Processing,2009,57(1):92-106.
[26] Viani F,Poli L,Oliveri G,Robol F,Massa A.Sparse scatterers imaging through approximated multitask compressive sensing strategies[J].Microwave and Optical Technology Letters,2013,55(7):1553-1558.
[27] Tipping M E,Faul A C.Fast marginal likelihood maximization for sparse Bayesian models[A].Proceedings of 9th International Workshop Artificial Intelligence and Statistics[C].Key West,FL:Mike Tipping,2003.1-13.

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

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

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 5

编辑推荐

Metrics

[1]	张清河, 于士奇, 时李萍, 张士惠. 对比源框架下的多任务贝叶斯压缩感知微波成像方法[J]. 电子学报, 2020, 48(11): 2208-2214.
[2]	刘月峰, 赵光权, 彭喜元. 多核相关向量机优化模型的锂电池剩余寿命预测方法[J]. 电子学报, 2019, 47(6): 1285-1292.
[3]	李龙军, 王布宏, 夏春和. 稀疏共形阵列天线方向图综合[J]. 电子学报, 2017, 45(1): 104-111.
[4]	杨国鹏;周欣;余旭初;陈伟. 基于相关向量机的高光谱影像混合像元分解[J]. 电子学报, 2010, 38(12): 2751-2756.
[5]	李东风;龚中麟. 六边形平面天线阵优化稀疏布阵研究[J]. 电子学报, 2002, 30(3): 376-380.