【百家大讲堂】第225期：环境依赖稀疏阵列设计研究进展

　　讲座题目：环境依赖稀疏阵列设计研究进展 

　　报  告 人：Moeness Amin

　　时  　 间：2019年7月19日上午9:00-11:00

　　地　   点：中关村校区信息科学实验楼205

　　主办单位：研究生院、信息学院

　　报名方式：登录外围滚球app：微信企业号---第二课堂---课程报名中选择“【百家大讲堂】第225期：环境依赖稀疏阵列设计研究进展 ”

 

【主讲人简介】

 Moeness Amin教授1984年于美国科罗拉多大学Boulder分校获得博士学位。从1985年开始，他成为维拉诺瓦（Villanova）大学的教职人员，现为该校电子和计算机工程系的教授和先进通信中心的主任。Amin博士是电气和电子工程师协会的会士、国际光学工程学会会士、工程技术学院会士；和欧洲信号处理协会会士。获得奖项主要包括：2017年富布赖特高级科学与技术杰出讲席教授、2016年亚历山大·冯·洪堡研究奖、2016年IET成就奖、2015年IEEE航空航天和电子系统协会Warren D White雷达工程卓越奖、2014年IEEE信号处理协会技术成就奖、欧洲信号处理协会颁发的2009年技术成就奖、以及IEEE第三届千禧奖章等。 Amin博士曾获选2003年和2004年IEEE信号处理协会的著名讲师，并且是富兰克林研究所科学与艺术委员会电气集群的前任主席。 Amin博士是15篇最佳论文奖的获得者，在信号处理理论和应用方面发表800多种期刊及会议论文，涉及无线通信，雷达，声纳，卫星导航，超声波，医疗保健和RFID等领域。他与人合着了21本书的章节，并由CRC出版社于2011年，2014年，2017年出版编辑了三本书，分别为《穿墙雷达成像》、《城市雷达压缩感知》、及《雷达室内监测》。

Moeness Amin received his Ph.D. degree in Electrical Engineering from the University of Colorado, Boulder, in 1984. Since 1985, he has been with the Faculty of the Department of Electrical and Computer Engineering, Villanova University, Villanova, PA, USA, where he became the Director of the Center for Advanced Communications, College of Engineering, in 2002. 

　　

Dr. Amin is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), Fellow of the International Society of Optical Engineering (SPIE), Fellow of the Institute of Engineering and Technology (IET), and a Fellow of the European Association for Signal Processing (EURASIP). He is the Recipient of: the 2017 Fulbright Distinguished Chair in Advanced Science and Technology, Recipient of the 2016 Alexander von Humboldt Research Award, Recipient of the 2016 IET Achievement Medal, Recipient of the 2014 IEEE Signal Processing Society Technical Achievement Award, Recipient of the 2009 Technical Achievement Award from the European Association for Signal Processing, and Recipient of the 2015 IEEE Aerospace and Electronic Systems Society Warren D White Award for Excellence in Radar Engineering.

　　

Dr. Amin is the Recipient of the IEEE Third Millennium Medal.  He was a Distinguished Lecturer of the IEEE Signal Processing Society, 2003-2004, and is the past Chair of the Electrical Cluster of the Franklin Institute Committee on Science and the Arts.  Dr. Amin is a Recipient of 15 Paper Awards, and has over 800 journal and conference publications in signal processing theory and applications, covering the areas of Wireless Communications, Radar, Sonar, Satellite Navigations, Ultrasound, Healthcare, and RFID.  He has co-authored 21 book chapters and is the Editor of three books titled, Through the Wall Radar Imaging, Compressive Sensing for Urban Radar, Radar for Indoor Monitoring, published by CRC Press in 2011, 2014, 2017, respectively. 

 

 

【讲座信息】

与均匀阵列相比，稀疏阵列设计使用较少的传感器即可实现相当的性能。与结构化阵列（如互质阵和嵌套阵）不同，可实现最佳性能标准（如最大信干比）的稀疏阵列设计是与环境相关的，它们的配置及波束形成系数权重随视野而变化。在本报告中，我们将从coarray的角度研究稀疏阵列的最大可扩展性，即最大化空间自相关延迟的数量。讨论了用于测向的主、被动稀疏阵列性能。然后将上述配置与稀疏阵列进行对比，在干扰环境中针对窄带和宽带干扰源均可实现最大化SINR。本报告还考虑了单点及多点干扰源，阵列孔径尺寸受限与不受限情况下的最佳性能分析。针对前者，我们引入了混合设计方法，在保证波束形成性能最优的前提下，设计一种完全可扩展的阵列。

Sparse array design can potentially achieve comparable performance over uniform array counterparts with a fewer sensors. Unlike structured arrays, such as coprime and nested arrays, sparse arrays designed to achieve optimum performance criterion, like maximum signal-to-interference plus noise ratio (MaxSINR), are environmental-dependent and their configurations as well as their beamformer weights change with the underlying field of view. In this tutorial, we review sparse arrays from the coarray perspective that strives for full augumentability, i.e.,maximizing the number of spatial autocorrelation lags. In this respect, we discuss sparse array performance for direction finding and also address the passive and active arrays. We then contrast these configurations with sparse arrays that achieve MaxSINR for both narrowband and wideband sources operating in an interference-active environment. The tutorial also considers both single point source and multiple point sources. We cover the two important cases where the array aperture size is constrained and unconstrained, and demonstrate optimum performance in both cases. For the former, and with a limited aperture, we introduce a hybrid design that seeks a full augumentable array which at the same time optimizes beamformer performance. The problem is formulated as quadratically constraint quadratic program, with the cost function penalized with weighted l1-norm squared of the beamformer weight vector. The wideband problem is tackled by two different approaches, one includes a delay line filter implementation and the other one is the DFT approach.