By Masaaki Nagahara, Hiroshima University, Japan, nagahara@ieee.org

Publication Date: 07 Apr 2025

Suggested Citation: Masaaki Nagahara (2025), "Compressed Sensing Approach to Systems and Control", Boston-Delft: now publishers, http://dx.doi.org/10.1561/9781638285052

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Description

Compressed sensing, also known as sparse representation or sparse modeling, has experienced substantial growth in research fields such as signal processing, machine learning, and statistics. In recent years, this powerful tool has been successfully applied to the design of control systems.

This book provides a comprehensive guide to compressed sensing-based techniques, focusing primarily on their application to systems and control. This book is intended for graduate students and researchers who already have a foundational understanding of basic calculus and linear algebra. Its primary objective is to equip readers with the practical skills to apply compressed sensing techniques to a range of engineering problems, with a particular emphasis on systems and control. It presents a comprehensive collection of efficient algorithms for addressing the problems discussed in the text. Moreover, the book includes accompanying Python programs, which enable readers to actively experiment with these algorithms first-hand. By engaging with these practical examples, readers will develop a deeper understanding of compressed sensing techniques and their applications to systems and control.

This book provides a comprehensive guide to compressed sensing-based techniques, focusing primarily on their application to systems and control. This book is intended for graduate students and researchers who already have a foundational understanding of basic calculus and linear algebra. Its primary objective is to equip readers with the practical skills to apply compressed sensing techniques to a range of engineering problems, with a particular emphasis on systems and control. It presents a comprehensive collection of efficient algorithms for addressing the problems discussed in the text. Moreover, the book includes accompanying Python programs, which enable readers to actively experiment with these algorithms first-hand. By engaging with these practical examples, readers will develop a deeper understanding of compressed sensing techniques and their applications to systems and control.