000			01956nam a2200265Ia 4500
001			TB625
003			IN-BhIIT
005			20210827144236.0
008			151217s9999 xx 000 0 eng d
020			_a9788120343597
040			_aIN-BhIIT
041			_aeng
082	0	0	_a515.7222 _bSTO/S
100	1		_aStoica, Petre _eauthor _9288
245	1	0	_aSpectral analysis of signals / _cby Petre Stoica and Randolph Moses
260			_aNew Delhi. : _bPrentice Hall, _c1998.
300			_axxii, 452 p. : _bill. ; _c25 cm.
504			_aIncludes bibliographical references and index
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 lecture, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.
650			_aSpectral theory (Mathematics) _92100
650			_aSignals _9915
650			_aElectrical Sciences _97057
700	1		_aMoses, Randolph L. _97058
942			_cTB _04
999			_c5789 _d5789