【百家大講堂】第225期：環(huán)境依賴稀疏陣列設(shè)計(jì)研究進(jìn)展

　　講座題目：環(huán)境依賴稀疏陣列設(shè)計(jì)研究進(jìn)展 

　　報(bào)  告 人：Moeness Amin

　　時(shí)  　 間：2019年7月19日上午9:00-11:00

　　地　   點(diǎn)：中關(guān)村校區(qū)信息科學(xué)實(shí)驗(yàn)樓205

　　主辦單位：研究生院、信息學(xué)院

　　報(bào)名方式：登錄北京理工大學(xué)微信企業(yè)號(hào)---第二課堂---課程報(bào)名中選擇“【百家大講堂】第225期：環(huán)境依賴稀疏陣列設(shè)計(jì)研究進(jìn)展 ”

 

【主講人簡(jiǎn)介】

 Moeness Amin教授1984年于美國(guó)科羅拉多大學(xué)Boulder分校獲得博士學(xué)位。從1985年開始，他成為維拉諾瓦（Villanova）大學(xué)的教職人員，現(xiàn)為該校電子和計(jì)算機(jī)工程系的教授和先進(jìn)通信中心的主任。Amin博士是電氣和電子工程師協(xié)會(huì)的會(huì)士、國(guó)際光學(xué)工程學(xué)會(huì)會(huì)士、工程技術(shù)學(xué)院會(huì)士；和歐洲信號(hào)處理協(xié)會(huì)會(huì)士。獲得獎(jiǎng)項(xiàng)主要包括：2017年富布賴特高級(jí)科學(xué)與技術(shù)杰出講席教授、2016年亞歷山大·馮·洪堡研究獎(jiǎng)、2016年IET成就獎(jiǎng)、2015年IEEE航空航天和電子系統(tǒng)協(xié)會(huì)Warren D White雷達(dá)工程卓越獎(jiǎng)、2014年IEEE信號(hào)處理協(xié)會(huì)技術(shù)成就獎(jiǎng)、歐洲信號(hào)處理協(xié)會(huì)頒發(fā)的2009年技術(shù)成就獎(jiǎng)、以及IEEE第三屆千禧獎(jiǎng)?wù)碌取?Amin博士曾獲選2003年和2004年IEEE信號(hào)處理協(xié)會(huì)的著名講師，并且是富蘭克林研究所科學(xué)與藝術(shù)委員會(huì)電氣集群的前任主席。 Amin博士是15篇最佳論文獎(jiǎng)的獲得者，在信號(hào)處理理論和應(yīng)用方面發(fā)表800多種期刊及會(huì)議論文，涉及無線通信，雷達(dá)，聲納，衛(wèi)星導(dǎo)航，超聲波，醫(yī)療保健和RFID等領(lǐng)域。他與人合著了21本書的章節(jié)，并由CRC出版社于2011年，2014年，2017年出版編輯了三本書，分別為《穿墻雷達(dá)成像》、《城市雷達(dá)壓縮感知》、及《雷達(dá)室內(nèi)監(jiān)測(cè)》。

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. 

 

 

【講座信息】

與均勻陣列相比，稀疏陣列設(shè)計(jì)使用較少的傳感器即可實(shí)現(xiàn)相當(dāng)?shù)男阅堋Ｅc結(jié)構(gòu)化陣列（如互質(zhì)陣和嵌套陣）不同，可實(shí)現(xiàn)最佳性能標(biāo)準(zhǔn)（如最大信干比）的稀疏陣列設(shè)計(jì)是與環(huán)境相關(guān)的，它們的配置及波束形成系數(shù)權(quán)重隨視野而變化。在本報(bào)告中，我們將從coarray的角度研究稀疏陣列的最大可擴(kuò)展性，即最大化空間自相關(guān)延遲的數(shù)量。討論了用于測(cè)向的主、被動(dòng)稀疏陣列性能。然后將上述配置與稀疏陣列進(jìn)行對(duì)比，在干擾環(huán)境中針對(duì)窄帶和寬帶干擾源均可實(shí)現(xiàn)最大化SINR。本報(bào)告還考慮了單點(diǎn)及多點(diǎn)干擾源，陣列孔徑尺寸受限與不受限情況下的最佳性能分析。針對(duì)前者，我們引入了混合設(shè)計(jì)方法，在保證波束形成性能最優(yōu)的前提下，設(shè)計(jì)一種完全可擴(kuò)展的陣列。

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.