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£75.84
Springer Principles and Applications of RELAX: A Robust and Universal Estimator
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Description
Product Description The multiple signal demixing and parameter estimation problems that result from the impacts of background noise and interference are issues that are frequently encountered in the fields of radar, sonar, communications, and navigation. Research in the signal processing and control fields has always focused on improving the estimation performance of parameter estimation methods at low SNR and maintaining the robustness of estimations in the presence of model errors. This book presents a universal and robust relaxation estimation method (RELAX), and introduces its basic principles and applications in the fields of classical line spectrum estimation, time of delay estimation, DOA estimation, and radar target imaging. This information is explained comprehensively and in great detail, and uses metaphors pertaining to romantic relationships to visualize the basic problems of parameter estimation, the basic principles of the five types of classical parameter estimation methods, and the relationships between these principles. The book serves as a reference for scientists and technologists in the fields of signal processing and control, while also providing relevant information for graduate students in the related fields. From the Back Cover The multiple signal demixing and parameter estimation problems that result from the impacts of background noise and interference are issues that are frequently encountered in the fields of radar, sonar, communications, and navigation. Research in the signal processing and control fields has always focused on improving the estimation performance of parameter estimation methods at low SNR and maintaining the robustness of estimations in the presence of model errors. This book presents a universal and robust relaxation estimation method (RELAX), and introduces its basic principles and applications in the fields of classical line spectrum estimation, time of delay estimation, DOA estimation, and radar target imaging. This information is explained comprehensively and in great detail, and uses metaphors pertaining to romantic relationships to visualize the basic problems of parameter estimation, the basic principles of the five types of classical parameter estimation methods, and the relationships between these principles. The book serves as a reference for scientists and technologists in the fields of signal processing and control, while also providing relevant information for graduate students in the related fields. About the Author Renbiao Wu is the Tianjin Professor and the Director of the Tianjin Key Lab for Advanced Signal Processing at the Civil Aviation University of China. He received his B.Sc. and M.Sc. in Electrical Engineering from the Northwest Polytechnic University in 1988 and 1991 respectively, and his Ph.D. in Electrical Engineering from Xidian University in 1994. He has worked at the Imperial College of London, the University of Florida, and Virginia Tech as a Distinguished Research Scholar, Visiting Professor, and Postdoctoral Fellow for 5 years. His research interests include adaptive array signal processing and spectral estimation, especially in regards to their applications in GNSS and radar. He has published over 300 peer-reviewed papers, and ten plus books and book chapters. He is the recipient of the Chinese National Outstanding Young Investigator Award in 2003. Qiongqiong Jia is a lecturer of the Tianjin Key Lab for Advanced Signal Processing at the Civil Aviation University of China. She received her B.Sc. and M.Sc. from the Civil Aviation University of China in 2008 and 2011, and her specialized master degree in navigation engineering from ENAC in France in 2015. Her research interests include adaptive array signal processing and spectral estimation regarding their applications to GNSS. She has published 20 papers, and co-authored three books and book chapters. Lei Yang is currently an associate professor of Tianjin Key Lab for
Product Specifications
- Brand
- Springer
- Format
- hardcover
- ASIN
- 9811369313
- Domain
- Amazon UK
- Release Date
- 09 April 2019
- Listed Since
- 01 February 2019
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