[PDF] Icassp 90 Spectral Estimation Underwater Signal Processing eBook

Author : Joseph C. Hassab
Publisher : CRC Press
Page : 374 pages
File Size : 30,81 MB
Release : 2018-01-18
Category : Technology & Engineering
ISBN : 1351094343

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A systematic and integrated account of signal and data processing with emphasis on the distinctive marks of the ocean environment is provided in this informative text. Underwater problems such as space-time processing relations vs. disjointed ones, processing of passive observations vs. active ones, time delay estimation vs. frequency estimation, channel effects vs. transparent ones, integrated study of signal, data, and channel processing vs. separate ones, are highlighted. The book provides the beginner with a concise presentation of the essential concepts, defines the basic computational steps, and gives the mature reader an advanced view of underwater systems and the relationships among their building blocks. It presents the needed topics on applied estimation theory within the underwater systems context. Included are topics in linear and nonlinear filtering, spectral analysis, generalized correlation, cepstrum and complex demodulation, Cramer-Rao Bounds, maximum likelihood, weighted least-squares, Kalman filtering, expert systems, wave propagation and their use, as well as their performance in applications to canonical ocean problems. The applications center on the definition, analysis, and solution implementations to representative underwater signal analysis problems dealing with signals estimation, their location and motion. The potential limitations and pitfalls of the implementations are delineated in homogeneous, noisy, interfering, inhomogeneous, multipath, distortions, and/or dispersive channels.

Author : Yanwei Wang
Publisher : Morgan & Claypool Publishers
Page : 108 pages
File Size : 19,3 MB
Release : 2005
Category : Computers
ISBN : 1598290002

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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.