Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend…mehr
Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and accurate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.
Yanwei Wang received the B.Sc. degree in electrical engineering from the Beijing University of Technology, China, in 1997 and the M.Sc. degree, again in electrical engineering from the University of Florida, Gainesville, in 2001. Since January 2000, he has been a research assistant with the Department of Electrical and Com puter Engineering, University of Florida, where he received the Ph.D. degree in December 2004. Currently, he is with the R&D group of Diagnostic Ultrasound Corp. His research interests include spectral estimation, medical tomographic imaging, and radar/array signal processing. Jian Li received the M.Sc. and Ph.D. degrees in electrical engineering from The Ohio State University, Columbus, in 1987 and 1991, respectively. From April 1991 to June 1991, she was an Adjunct Assistant Professor with the Department of Electrical Engineering, The Ohio State University, Columbus. From July 1991 to June 1993, she was an Assistant Professor with the Department of Electrical Engineering, University of Kentucky, Lexington. Since August 1993, she has been with the Department of Electrical and Computer Engineering, University of Florida, Gainesville, where she is currently a Professor. Her current research interests include spectral estimation, array signal processing, and their applications. Dr. Li is a member of Sigma Xi and Phi Kappa Phi. She received the 1994 Petre Stoica (F'94) received the D.Sc. degree in automatic control from the Polytechnic Institute of Bucharest (BPI), Bucharest, Romania, in 1979 and an honorary doctorate degree in science from Uppsala University (UU), Uppsala, Sweden, in 1993. He is Professor of system modeling with the Department of Systems and Control at UU. Previously, he was a Professor of system identification and signal processing with the Faculty of Automatic Control and Computers at BPI. He held longer visiting positions with Eindhoven University of Technology, Eindhoven, The Netherlands; Chalmers University of Technology,Gothenburg, Sweden (where he held a Jubilee Visiting Professorship); UU; The University of Florida, Gainesville; and Stanford University, Stanford, CA. His main scientific interests are in the areas of system identification, time series analysis and prediction, statistical signal and array processing, spectral analysis, wireless communications, and radar signal processing. Dr. Stoica was a corecipient of the IEEE ASSP Senior Award for a paper on statistical aspects of array signal processing. He was also a recipient of the Technical Achievement Award of the IEEE Signal Processing Society for fundamental con tributions to statistical signal processing with applications in time series analysis, system identification, and array signal processing. In 1998, he was the recipient of a Senior Individual Grant Award of the Swedish Foundation for Strategic Research. He was also a corecipient of the 1998 EURASIP Best Paper Award for Signal Pro cessing for a work on parameter estimation of exponential signals with time-varying amplitude, a 1999 IEEE Signal Processing Society Best Paper Award for a paper on parameter and rank estimation of reduced-rank regression, a 2000 IEEE Third Millennium Medal, and the 2000 W. R. G. Baker Prize Paper Award for a paper on maximum likelihood methods for radar. He was a member of the international program committees of many topical conferences. From 1981 to 1986, he was the Director of the International Time Series Analysis and Forecasting Society, and he has been a member of the IFAC Technical Committee on Modeling, Identification, and Signal Processing since 1994. He is also a member of the Romanian Academy and a Fellow of the Royal Statistical Society.
Inhaltsangabe
Introduction.- Linear Source Separation.- Nonlinear Separation.- Final Comments.- Statistical Concepts.- Online Software and Data.
