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Copyright: 2022
Pages: 392
ISBN: 9781630818920

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Description

This book provides a comprehensive overview of key optimization tools that can be used to design radar waveforms and adaptive signal processing strategies, assisting you in meeting the increased demands of sensing system proliferation. These methods include power-methodlike iterations, coordinate descent, majorization-minimization, block successive upper-bound minimization, and semidefinite programming. This book walks you through these optimization frameworks that achieve the desired design goal for waveform design as a solution to a constrained optimization problem, such as finite energy, unimodularity (or having constantmodulus), and finite or discrete-phase (potentially binary) alphabet, which are dictated by practical constraints of real systems. Focusing on a holistic approach rather than a problemspecific approach, the book shows what you need to formulate waveform design effectively and to understand the flexibility of the framework for adapting to your own specific needs.

 

By reading this book, you will have full access to the tools and knowledge required to design waveforms with optimized correlation/cross-correlation properties in different dimensions (e.g., time or space) for multiple radar configurations such as SISO/SIMO and MIMO radars, while considering spectral constraints for emerging topics such as cognitive radar, coexistence with communications, and mitigation of potential Doppler and quantization errors. It also includes sample software programs to assist you in generating the described solutions. This is a detailed handbook for industry researchers, scientists, and designers, including medical, marine, defense, and automotive companies, due to its unique style of covering mathematical results as well as their applications from various areas. With many exercise problems, the book is also an excellent resource for advanced courses on radar signal processing.

Table Of Contents

Need for practical signal design, Convex and non-convex optimization, Power method-like iterations Majorization minimization (MM) methods, Coordinate descent (CD) and Block Successive Upper-Bound Minimization (BSUM) methods, Other optimization methods, Deep learning for radar, High Resolution and 4D imaging MIMO Radars for Automotive applications , Waveform design in Spectrum sharing applications, Indoor applications, Optimal transmit signal design for Space-Time Adaptive Processing (STAP) in MIMO radar systems, Cognitive radar, prototype, and implementation

Author

  • Mohammad Alaee-Kerahroodi

    received a Ph.D. in telecommunication engineering from the Department of Electrical and Computer Engineering at Isfahan University of Technology, Iran, in 2017. During his doctoral studies and in 2016, he worked as a visiting researcher at the University of Naples “Federico II” in Italy. After receiving his doctorate, he began working as a research associate at SnT - Interdisciplinary Centre for Security, Reliability, and Trust, in the University of Luxembourg, LUXEMBOURG. At the present, he works as a research scientist at SnT and leads the prototyping and lab activities for the SPARC (Signal Processing Applications in Radar and Communications) research group. Along with conducting academic research in the field of radar waveform design and array signal processing, he is working on novel solutions for mmWave MIMO radar systems. Dr. Alaee has more than 12 years of experience working with a variety of radar systems, including automotive, ground surveillance, air surveillance, weather, passive, and marine.

  • Prabhu Babu

    received his B. Tech degree from Madras Institute of Technology in Electronics in 2005. He then obtained his M. Tech degree in Radio frequency design technology (RFDT) from Centre for Applied Research in Electronics (CARE), IIT Delhi in 2007. He finished his doctor of philosophy from Uppsala University, Sweden in 2012. From January 2013 to December 2015, he was at Hong Kong University of Science and Technology (HKUST), Hong Kong doing his postdoctoral research. In January 2016, he joined CARE, IIT Delhi as an Associate Professor.

  • Mojtaba Soltanalian

    (Senior Member, IEEE) received the Ph.D. degree in electrical engineering (with specialization in signal processing) from the Department of Information Technology, Uppsala University, Sweden, in 2014. He is currently with the faculty of the Electrical and Computer Engineering Department, University of Illinois at Chicago (UIC), Chicago, IL, USA. Before joining UIC, he 363 364 held research positions with the Interdisciplinary Centre for Security, Reliability and Trust (SnT, University of Luxembourg), and California Institute of Technology, Pasadena, CA, USA. His research interests include interplay of signal processing, learning and optimization theory, and specifically different ways the optimization theory can facilitate a better processing and design of signals for collecting information, communication, and also to form a more profound understanding of data, whether it is in everyday applications or in large-scale, complex scenarios. Dr. Soltanalian serves as an Associate Editor for IEEE Transactions on Signal Processing and as the Chair of the IEEE Signal Processing Society Chapter in Chicago. He was the recipient of the 2017 IEEE Signal Processing Society Young Author Best Paper Award, an also the 2018 European Signal Processing Association Best PhD Award.

  • M. R. Bhavani Shankar

    received Masters and Ph. D in Electrical Communication Engineering from Indian Institute of Science, Bangalore in 2000 and 2007 respectively. He was a Post Doc at the ACCESS Linnaeus Centre, Signal Processing Lab, Royal Institute of Technology (KTH), Sweden from 2007 to September 2009. He joined SnT in October 2009 as a Research Associate and is currently a Senior Research Scientist/ Assistant Professor at SnT leading the Radar Signal Processing activities.