● Optimization Techniques for Beamforming
Abstract: In this tutorial, we will review some useful optimization techniques for beamforming applications, including decomposition of a large array for efficient beamforming, solution robustness to DoA estimation errors (especially when the degree of freedom is limited), joint attention decoding and microphone array beamforming.
Zhi-Quan (Tom) Luo
Department of Electrical and Computer Engineering University of Minnesota, Twin Cites
Luo Zhi Quan (Tom) received his PhD Degree in Operations Research from Massachusetts Institute of Technology in 1989. From 1989 to 2003, he held a faculty position in the Department of Electrical and Computer Engineering, McMaster University, Canada and a Canada Research Chair in Information Processing. He moved to Minnesota in April 2003 and have since been a full professor in the Department of Electrical and Computer Engineering, University of Minnesota. He also hold an endowed ADC Chair in Digital Technology.
His general research interests include the theory, design and analysis of efficient optimization algorithms with application to data communication, wireless and optical networks and systems, and signal processing.
He is especially interested in computation/communication complexity issues arising from these problem areas. This includes both providing theoretical lower bounds on the complexity and designing efficient methods whose complexity closely match the lower bounds. His current research interest lies in the theory of multi-user communications and the application of optimization techniques to the design of multi-antenna communication systems.
● Four Decades of Array Signal Processing Research: An Optimization Relaxation Technique Perspective
Abstract: We currently witness that sensor array processing, more specifically direction-of-arrival (DoA) estimation, receives new momentum due to the emergence of new applications such as automotive radar, drone localization, and parametric channel estimation in Massive MIMO. This development is reinforced by the emergence of new powerful and affordable multiantenna hardware platforms. In this tutorial we provide a consistent overview of developments in four decades of sensor array processing techniques for DoA estimation, ranging from traditional super resolution techniques to modern sparse optimization based DoA estimation techniques. In our overview we take a modern optimization based view and retell the story of sensor array processing from the relaxation technique perspective. We will show, from that perspective, that constrained optimization problem formulations and problem relaxation techniques have always played an important role in the development of powerful DoA estimation methods. In this context we will introduce the partial relaxation technique that has been proposed as a new optimization based DoA estimation framework applying modern relaxation techniques to traditional multi-source estimation criteria to construct new estimators with excellent estimation performance at affordable computational complexity. In many senses it can be observed that the estimators designed under the partial relaxation framework admit new insights in existing methods of this well established field of research.
Communication Systems Group, Technische Universitat Darmstadt
Marius Pesavento received the Dipl.-Ing. degree in 1999 and the Dr.-Ing. degree in electrical engineering in 2005, both from Ruhr-University Bochum, Bochum, Germany, and the M.Eng. degree from McMaster University, Hamilton, ON, Canada, in 2000. Between 2005 and 2009, he held research positions in two start-up companies. In 2010, he became an Assistant Professor for robust signal processing and a Full Professor for communication systems in 2013 with the Department of Electrical Engineering and Information Technology, Technical University Darmstadt, Darmstadt, Germany.
His research interests include robust signal processing and adaptive beamforming, high-resolution sensor array processing, multiantenna and multiuser communication systems, distributed, sparse, and mixed-integer optimization techniques for signal processing and communications, statistical signal processing, spectral analysis, and parameter estimation. He served as an Associate Editor for the IEEE Transactions on Signal Processing during 2012–2016 and is currently a member of the Editorial Board of the EURASIP Signal Processing Journal. He is a member of the Sensor Array and Multichannel Technical Committee of the IEEE Signal Processing Society and the Special Area Teams Signal Processing for Communications and Networking and Signal Processing for Multisensor Systems of the EURASIP. He is the recipient of the 2003 ITG/VDE Best Paper Award, the 2005 Young Author Best Paper Award of the IEEE Transactions On Signal Processing, and the 2010 Best Paper Award of the CrownCOM Conference.
Minh Trinh Hoang
Communication Systems Group, Technische Universitat Darmstadt
Minh Trinh-Hoang received the M.Sc. degree in electrical engineering in 2017 from Technical University Darmstadt, Darmstadt, Germany, where he is currently working toward the Ph.D degree. His research interests include statistical signal processing, array processing, and numerical computation.
Blekinge Institute of Technology
Mats Viberg received the M.Sc. degree in applied mathematics in 1985 and the Ph.D. degree in automatic control from Linköping University, Linköping, Sweden, in 1989.
Dr. Viberg has served in various capacities in the IEEE Signal Processing Society, including the Chair with the Technical Committees on Signal Processing Theory and Methods and Sensor Array and Multichannel Signal Processing as well as member at large with the Board of Governors. He has also served in various capacities with the Swedish Research Council (VR). He was the recipient of the two paper awards from the IEEE Signal Processing Society, the Excellent Research Award from the VR, and the EURASIP European Group Technical Achievement Award. He is a member of the Royal Swedish Academy of Sciences and the Royal Society of Arts and Sciences in Gothenburg, Sweden.