Title:Sparse Array Beamforming and Its Applications
Speaker:Prof. Elias Aboutanios, Prof. Xiangrong Wang
Affiliation:University of New South Wales,Beihang University

Abstract: Sampling using a set of spatially distributed sensors finds extensive applications. The configuration of sensor arrays is a key parameter that plays a fundamental role in the sampling performance. Fully populated arrays contain significant redundancy, which allows for significant hardware and computational savings. Since the array configuration characterizes the structure of the spatial filters, sparse array design optimizes the sensor placement to achieve the desired performance. Then sparse arrays capitalize on the inherent redundancy to enable the reduction of system overhead while preserving the performance. As the number of sensors typically dictates the number of costly front-end processing chains, sparse arrays then open the door to save on their size, weight, and power (SWaP). Put another way, given the SWaP budget, sparse arrays can enhance the processing performance via optimizing array configuration or antenna placement. There are many applications which require different sparse arrays, ranging from Direction of Arrival (DOA) estimation to adaptive beamforming. Driven by the increasing importance of sparse arrays, research into sparse array design techniques continues unabated.

In this tutorial, we first review the fundamentals and concepts of array signal processing, including array fundamentals, conventional beamforming and DOA estimation, based on uniform linear array. Second, we talk about two main categories of sparse arrays, those are structured sparse arrays and unstructured sparse arrays. The former refers to the kind of sparse arrays where the antenna' locations exhibit a particular mathematical structure and are fixed once designed, such as nested arrays and coprime arrays for DOA estimation, and those for beampattern synthesis. Most literature on sparse arrays fall into the former case. The latter refers to the kind of reconfigurable sparse arrays which can extract the impinging interference and clutter statistics from the received data and changes its configuration in terms of the output Maximum Signal-to-Interference-plus-Noise-Ratio (MaxSINR) cognitively. The dynamic array reconfiguration of unstructured sparse arrays can be completed via antenna selection from a uniform counterpart. Therefore, a radio frequency (RF) switch network is mandated with two different structures of the fully-connected one and partially-connected. Finally, we discuss the sparse array beamforming in the popular application of integrated sensing and communications (ISAC), where the degrees of freedom (DoFs) provided by array configuration can not only enhance the performance of dual functionalities but also be utilized to embed communication information. The corresponding method is spatial index modulation, which endows sparse array with new application prospects.

Biograph:

Elias Aboutanios received the bachelor's degree in engineering from UNSW Australia in 1997, and the Ph.D. degree from the University of Technology Sydney (UTS), Australia, in 2003. From 2003 to 2007, he was a Research Fellow with the Institute for Digital Communications, University of Edinburgh, where he conducted research on space time adaptive processing for radar target detection. He is currently Professor at the School of Electrical Engineering and Telecommunications of the University of New South Wales. He is a recipient of the Best Oral Presentation Award (CISPBMEI'10), Teaching Excellence Award in 2011, Excellence in Research Supervision Award in 2014, the Australian Postgraduate Scholarship in 1998, Sydney Electricity Scholarship in 1994, and UNSW Co-Op Scholarship in 1993. He is the vice president of the IEEE SAM Technical Committee and is currently serving as an Associate Editor of the IEEE transactions on Signal Processing and IET Signal Processing. His research interests are in statistical signal processing, in particular signal detection and parameter estimation, for various applications such as radar, GNSS, smart grids, and nuclear magnetic resonance spectroscopy. He also runs various Space activities and projects and has established and led the UNSW-EC0 cubesat project, which culminated in the launch of the satellite in 2017.

Xiangrong Wang received the the B.Eng. and M.Eng. degrees in electrical engineering from the Nanjing University of Science and Technology, Nanjing, China, in 2009 and 2011, respectively, and the Ph.D. degree in signal processing from the University of New South Wales, Sydney, NSW, Australia, in 2015. From February to September 2016, she was a Postdoctoral Research Fellow with the Center for Advanced Communications, Villanova University, Villanova, PA, USA. She is currently a Professor with the School of Electronic and Information Engineering, Beihang University, Beijing, China. She is the recipient of the 2023 Barry Carlton Award of the IEEE AES Society. She was awarded the Marie Skłodowska-Curie action (MSCA) Individual Fellowship sponsored by the EU. She is a member of the IEEE SAM Technical Committee and is currently serving as an Associate Editor of the IEEE transactions on Radar Systems and Elsevier Digital Signal Processing. Her research interests include array signal processing, radar signal processing, integrated radar and communications, etc.