| 000 | 05088nam a2200949 i 4500 | ||
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| 001 | 6331043 | ||
| 003 | IEEE | ||
| 005 | 20191218152123.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151222s2012 nju ob 001 eng d | ||
| 020 |
_a9781118437926 _qebook |
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| 020 | _a9781118437919 | ||
| 020 | _a1118437918 | ||
| 020 |
_z1118437926 _qelectronic |
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| 020 |
_z9781118437902 _qelectronic |
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| 020 |
_z111843790X _qelectronic |
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| 020 |
_z9781283597074 _qMyiLibrary |
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| 020 |
_z1283597071 _qMyiLibrary |
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| 020 |
_z9781119973355 _qprint |
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| 024 | 7 |
_a10.1002/9781118437926 _2doi |
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| 035 | _a(CaBNVSL)mat06331043 | ||
| 035 | _a(IDAMS)0b0000648193dda8 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aTK5102.9. _bN36 2012eb |
|
| 082 | 0 | 4 |
_a621.3822 _223 |
| 100 | 1 |
_aNapolitano, Antonio, _d1964- |
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| 245 | 1 | 0 |
_aGeneralizations of cyclostationary signal processing : _bspectral analysis and applications / _cAntonio Napolitano. |
| 264 | 1 |
_aOxford : _bWiley-Blackwell, _c2012. |
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| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2012] |
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| 300 | _a1 PDF (xix, 480 pages). | ||
| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 | _aWiley - IEEE | |
| 504 | _aIncludes bibliographical references and index. | ||
| 505 | 0 | _aBackground -- Generalized Almost-Cyclostationary Processes -- Complements and Proofs on Generalized Almost-Cyclostationary Processes -- Spectrally Correlated Processes -- Complements and Proofs on Spectrally Correlated Processes -- Functional Approach for Signal Analysis -- Applications to Mobile Communications and Radar/Sonar. | |
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aThe relative motion between the transmitter and the receiver modifies the nonstationarity properties of the transmitted signal. In particular, the almost-cyclostationarity property exhibited by almost all modulated signals adopted in communications, radar, sonar, and telemetry can be transformed into more general kinds of nonstationarity. A proper statistical characterization of the received signal allows for the design of signal processing algorithms for detection, estimation, and classification that significantly outperform algorithms based on classical descriptions of signals. Generalizations of Cyclostationary Signal Processing addresses these issues and includes the following key features: . Presents the underlying theoretical framework, accompanied by details of their practical application, for the mathematical models of generalized almost-cyclostationary processes and spectrally correlated processes; two classes of signals finding growing importance in areas such as mobile communications, radar and sonar.. Explains second- and higher-order characterization of nonstationary stochastic processes in time and frequency domains.. Discusses continuous- and discrete-time estimators of statistical functions of generalized almost-cyclostationary processes and spectrally correlated processes.. Provides analysis of mean-square consistency and asymptotic Normality of statistical function estimators.. Offers extensive analysis of Doppler channels owing to the relative motion between transmitter and receiver and/or surrounding scatterers.. Performs signal analysis using both the classical stochastic-process approach and the functional approach, where statistical functions are built starting from a single function of time. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/22/2015. | ||
| 650 | 0 | _aSignal processing. | |
| 650 | 0 | _aSpectrum analysis. | |
| 650 | 0 | _aCyclostationary waves. | |
| 655 | 0 | _aElectronic books. | |
| 695 | _aAntenna radiation patterns | ||
| 695 | _aBandwidth | ||
| 695 | _aClamps | ||
| 695 | _aConvergence | ||
| 695 | _aConvolution | ||
| 695 | _aCorrelation | ||
| 695 | _aDelay | ||
| 695 | _aDensity functional theory | ||
| 695 | _aDistribution functions | ||
| 695 | _aDoppler effect | ||
| 695 | _aEstimation | ||
| 695 | _aFourier series | ||
| 695 | _aFourier transforms | ||
| 695 | _aFrequency estimation | ||
| 695 | _aHarmonic analysis | ||
| 695 | _aIndexes | ||
| 695 | _aJoints | ||
| 695 | _aMathematical model | ||
| 695 | _aMobile communication | ||
| 695 | _aProbabilistic logic | ||
| 695 | _aRandom variables | ||
| 695 | _aReceivers | ||
| 695 | _aReliability | ||
| 695 | _aSections | ||
| 695 | _aSignal analysis | ||
| 695 | _aSignal processing | ||
| 695 | _aSpectral analysis | ||
| 695 | _aStochastic processes | ||
| 695 | _aTime frequency analysis | ||
| 695 | _aTime series analysis | ||
| 710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. |
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| 710 | 2 |
_aWiley InterScience (Online service), _epublisher. |
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| 776 | 0 | 8 |
_iPrint version: _z9781119973355 |
| 830 | 0 | _aWiley - IEEE | |
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6331043 |
| 999 |
_c42425 _d42425 |
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