000			03144nam a22004335i 4500
999			_c387948 _d387948 _x1
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003			ES-MaUEC
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007			cr nn 008mamaa
008			230511s2005 sz | o |||| 0|eng d
020			_a9783031025259
024	7		_a10.1007/978-3-031-02525-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA320 _b2005 EB
100	1		_aWang, Yanwei _d1973- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688476
245	1	0	_aSpectral Analysis of Signals : _bThe Missing Data Case _cby Yanwei Wang, Jian Li, Petre Stoica
250			_a1st edition 2005
264		1	_aCham _bSpringer International Publishing _c2005
300			_a1 recurso en línea (VIII, 99 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Signal Processing _x1932-1694
505	0		_aIntroduction -- Linear Source Separation -- Nonlinear Separation -- Final Comments -- Statistical Concepts -- Online Software and Data.
520			_aSpectral 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.
988			_aSynthesis Collection of Technology_2005
650		7	_2embne _9150608 _aProceso de señales _xModelos matemáticos
650		7	_2embne _9686387 _aEstadística no paramétrica
700	1		_aLi, Jian _d1965- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688477
700	1		_aStoica, Petre _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688478
776	0	8	_iPrinted edition: _z9783031013973
776	0	8	_iPrinted edition: _z9783031036538
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02525-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eIG _zSI