Del i serien Radar, Sonar and Navigation

Radar Waveform Design based on Optimization Theory

Inbunden, Engelska, 2020

AvGuolong, Cui,Guolong Cui,Antonio De Maio,Alfonso Farina,Jian Li

2 229 kr

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This book provides an overview of radar waveform synthesis obtained as the result of computational optimization processes and covers the most challenging application fields. The book balances a practical point of view with a rigorous mathematical approach corroborated with a wealth of numerical study cases and some real experiments. Additionally, the book has a cross-disciplinary approach because it exploits cross-fertilization with the recent research and discoveries in optimization theory. The material of the book is organized into ten chapters, each one completed with a comprehensive list of references.The following topics are covered: recent advances of binary sequence designs and their applications; quadratic optimization for unimodular sequence synthesis and applications; a computational design of phase-only (possibly binary) sequences for radar systems; constrained radar code design for spectrally congested environments via quadratic optimization; robust transmit code and receive filter design for extended targets detection in clutter; optimizing radar transceiver for Doppler processing via non-convex programming; radar waveform design via the majorization-minimization framework; Lagrange programming neural network for radar waveform design; cognitive local ambiguity function shaping with spectral coexistence and experiments; and relative entropy based waveform design for MIMO radar.Targeted at an audience of radar engineers and researchers, this book provides thorough and up-to-date coverage of optimisation theory for radar waveform design.

Produktinformation

Utgivningsdatum2020-11-04
Mått156 x 234 x 23 mm
Vikt703 g
FormatInbunden
SpråkEngelska
SerieRadar, Sonar and Navigation
Antal sidor352
FörlagInstitution of Engineering and Technology
ISBN9781785619434

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Guolong Cui is a professor with the University of Electronic Science and Technology of China, Chengdu. His current research interests include waveform optimization theory and algorithms, adaptive signal processing, and statistical signal processing with emphasis on radars.Antonio De Maio is a professor with the University of Naples Federico II. His research interest is in the field of statistical signal processing, with emphasis on radar detection and optimization theory applied to radar signal processing. Dr. De Maio is a fellow member of IEEE and the recipient of the 2010 IEEE Fred Nathanson Memorial Award as the young (less than 40 years of age) AESS Radar Engineer 2010.Alfonso Farina (fellow of IEEE, FREng, FIET, fellow of EURASIP), Selex ES (retired) is a consultant in Roma, Italy. Dr. Farina is the author of more than 800 peer-reviewed technical publications and of books and monographs (published worldwide). Awards he has received recently include the IET Achievement Medal for outstanding contributions to radar system design, signal, data and image processing, and data fusion (2014) and Christian Hülsmeyer Award from the German Institute of Navigation (DGON) (2019).Jian Li is a professor with the Department of Electrical and Computer Engineering, University of Florida, Gainesville, USA. In fall 2007, she was on sabbatical leave at MIT, Cambridge, Massachusetts. Her current research interests include spectral estimation, statistical and array signal processing, and their applications. Dr. Li is a fellow of IEEE and a fellow of IET.

Chapter 1: On recent advances of binary sequence designs and their applicationsChapter 2: Quadratic optimization for unimodular sequence synthesis and applicationsChapter 3: A computational design of phase-only (possibly binary) sequences for radar systemsChapter 4: Constrained radar code design for spectrally congested environments via quadratic optimizationChapter 5: Robust transmit code and receive filter design for extended targets detection in clutterChapter 6: Optimizing radar transceiver for Doppler processing via non-convex programmingChapter 7: Radar waveform design via the majorization-minimization frameworkChapter 8: Lagrange programming neural network for radar waveform designChapter 9: Cognitive local ambiguity function shaping with spectral coexistence and experimentsChapter 10: Relative entropy-based waveform design for MIMO radar