000			03067nam a22004695i 4500
001			978-0-387-76544-0
003			DE-He213
005			20161121230533.0
007			cr nn 008mamaa
008			110402s2008 xxu| s |||| 0|eng d
020			_a9780387765440 _9978-0-387-76544-0
024	7		_a10.1007/978-0-387-76544-0 _2doi
050		4	_aTK1-9971
072		7	_aTJK _2bicssc
072		7	_aTEC041000 _2bisacsh
082	0	4	_a621.382 _223
100	1		_aLevy, Bernard C. _eauthor.
245	1	0	_aPrinciples of Signal Detection and Parameter Estimation _h[electronic resource] / _cby Bernard C. Levy.
264		1	_aBoston, MA : _bSpringer US, _c2008.
300			_a664 p. 101 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aI Foundations -- Binary and Mary Hypothesis Testing -- Tests with Repeated Observations -- Parameter Estimation Theory -- Composite Hypothesis Testing -- Robust Detection -- II Gaussian Detection -- Karhunen Loeve Expansion of Gaussian Processes -- Detection of Known Signals in Gaussian Noise -- Detection of Signals with Unknown Parameters -- Detection of Gaussian Signals in WGN -- EM Estimation and Detection of Gaussian Signals with unknown parameters -- III Markov Chain Detection -- Detection of Markov Chains with Known Parameters -- Detection of Markov Chains with Unknown Parameters.
520			_aThis new textbook is for contemporary signal detection and parameter estimation courses offered at the advanced undergraduate and graduate levels. It presents a unified treatment of detection problems arising in radar/sonar signal processing and modern digital communication systems. The material is comprehensive in scope and addresses signal processing and communication applications with an emphasis on fundamental principles. In addition to standard topics normally covered in such a course, the author incorporates recent advances, such as the asymptotic performance of detectors, sequential detection, generalized likelihood ratio tests (GLRTs), robust detection, the detection of Gaussian signals in noise, the expectation maximization algorithm, and the detection of Markov chain signals. Numerous examples and detailed derivations along with homework problems following each chapter are included.
650		0	_aEngineering.
650		0	_aInformation theory.
650		0	_aStatistics.
650		0	_aElectrical engineering.
650	1	4	_aEngineering.
650	2	4	_aCommunications Engineering, Networks.
650	2	4	_aSignal, Image and Speech Processing.
650	2	4	_aInformation and Communication, Circuits.
650	2	4	_aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387765426
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-76544-0
912			_aZDB-2-ENG
950			_aEngineering (Springer-11647)
999			_c500252 _d500252