Prof. Zai Yang

Keynote Speech Title: Multichannel Line Spectral Estimation via Convex Optimization: Methods andTheory

Speaker's Bio:
Zai Yang received the in mathematics in 2007 and the in applied mathematics in 2009 both from Sun Yat-sen (Zhongshan) University, China, and the Ph.D degree in electrical and electronic engineering in 2014 from Nanyang Technological University (NTU), Singapore. After a Research Fellow experience at NTU, he joined the School of Automation, Nanjing University of Science and Technology, China, in December 2015 as a Professor. He has published over 30 papers on international journals and conferences such as IEEE Trans. Information Theory and IEEE Trans. Signal Processing. He is a leading tutorial presenter at EUSIPCO 2017 and is serving on the editorial board of Signal Processing (Elsevier). His research interests include compressed sensing, optimization theory and their applications in signal and information processing and machine learning.

Keynote Speech Abstract:
Line spectral estimation or frequency estimation is a fundamental problem in statistical signal processing. It aims at representing a natural signal as superposition of several sinusoidal waves. The use of multichannel data for line spectral estimation arises in applications such as array processing, radar, structural health monitoring, wireless communications, and more. In the past decade, sparse representation and compressed sensing techniques have demonstrated their superiority in flexibility, accuracy and robustness in comparison with conventional nonparametric and parametric methods. However, their performance is usually questionable due to approximation introduced and lack of theoretical analysis. In this talk, we introduce the most recent developments in this area using atomic norm. As continuous analogs of L1 norm minimization approaches, atomic norm methods exploit signal sparsity, work directly in the continuous parameter domain, can be implemented using convex optimization, and have provable theoretical guarantees. The role of multiple channels isdiscussed by analysing their worst-case and average-case performances.