Tutorials

Sparse Sensing for Statistical Inference

Prof. Geert Leus and Dr. Sundeep Chepuri, TU Delft, The Netherlands

Abstract:

Ubiquitous sensors generate prohibitively large data sets. Large volumes of such data are nowadays generated by a variety of applications such as imaging platforms and mobile devices, surveillance cameras, social networks, power networks, to list a few. In this era of data deluge, it is of paramount importance to gather only the data that is informative for a specific task in order to limit the required sensing cost, as well as the related costs of storing, processing, or communicating the data. The main goal of this tutorial is therefore to present topics that transform classical sensing methods, often based on Nyquist-rate sampling, to more structured low-cost sparse sensing mechanisms designed for specific inference tasks. More specifically, we present fundamental tools to achieve the lowest sensing cost with a guaranteed performance for the task at hand. We will give an overview of ongoing research in task-cognizant sparse sensing and present the advantages over the state of the art in this field, known as compressive sensing. We focus on structured sampler designs for fundamental signal processing tasks, such as estimation (including filtering/control) and detection, in the classical as well as the Bayesian setting. We further extend sparse sensing to concepts such as covariance sensing, outlier rejection and distributed sensing. Throughout this tutorial we apply the developed theory to a number of diverse examples related to localization, field estimation, spectrum sensing, and so on. The tutorial is very timely and touches upon problems in big data analytics as well as compressive sensing.

Biographies:

Geert Leus received the MSc and PhD degree in Applied Sciences from the Katholieke Universiteit Leuven, Belgium, in June 1996 and May 2000, respectively. Currently, Geert Leus is an "Antoni van Leeuwenhoek" Full Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the Delft University of Technology, The Netherlands. His research interests are in the area of signal processing for communications. Geert Leus received a 2002 IEEE Signal Processing Society Young Author Best Paper Award and a 2005 IEEE Signal Processing Society Best Paper Award. He is a Fellow of the IEEE and a Fellow of EURASIP. Geert Leus was the Chair of the IEEE Signal Processing for Communications and Networking Technical Committee, and an Associate Editor for the IEEE Transactions on Signal Processing, the IEEE Transactions on Wireless Communications, the IEEE Signal Processing Letters, and the EURASIP Journal on Advances in Signal Processing. Currently, he is a Member-at-Large to the Board of Governors of the IEEE Signal Processing Society and a member of the IEEE Sensor Array and Multichannel Technical Committee. He finally serves as the Editor in Chief of the EURASIP Journal on Advances in Signal Processing.

Sundeep Prabhakar Chepuri was born in India in 1986. He received his M.Sc. degree (cum laude) in electrical engineering and Ph.D. degree (cum laude) from the Delft University of Technology, The Netherlands, in July 2011 and January 2016, respectively. He has held positions at Robert Bosch, India, during 2007-2009, and Holst Centre/imec-nl, The Netherlands, during 2010-2011. He is currently a postdoctoral scholar with the Circuits and Systems group at the Faculty of Electrical Engineering, Mathematics and Computer Science of the Delft University of Technology, The Netherlands. His general research interest lies in the field of mathematical signal processing, statistical inference, sensor networks, and wireless communications. He was a recipient of the Best Student Paper Award at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) in 2015.