000 04149nam a22005055i 4500
001 978-3-031-02527-3
003 DE-He213
005 20240730163953.0
007 cr nn 008mamaa
008 220601s2008 sz | s |||| 0|eng d
020 _a9783031025273
_9978-3-031-02527-3
024 7 _a10.1007/978-3-031-02527-3
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
_2thema
082 0 4 _a620
_223
100 1 _aVaidyanathan, P.P.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_972041
245 1 4 _aThe Theory of Linear Prediction
_h[electronic resource] /
_cby P.P. Vaidyanathan.
250 _a1st ed. 2008.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2008.
300 _aXIV, 183 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Signal Processing,
_x1932-1694
505 0 _aIntroduction -- The Optimal Linear Prediction Problem -- Levinson's Recursion -- Lattice Structures for Linear Prediction -- Autoregressive Modeling -- Prediction Error Bound and Spectral Flatness -- Line Spectral Processes -- Linear Prediction Theory for Vector Processes -- Appendix A: Linear Estimation of Random Variables -- B: Proof of a Property of Autocorrelations -- C: Stability of the Inverse Filter -- Recursion Satisfied by AR Autocorrelations.
520 _aLinear prediction theory has had a profound impact in the field of digital signal processing. Although the theory dates back to the early 1940s, its influence can still be seen in applications today. The theory is based on very elegant mathematics and leads to many beautiful insights into statistical signal processing. Although prediction is only a part of the more general topics of linear estimation, filtering, and smoothing, this book focuses on linear prediction. This has enabled detailed discussion of a number of issues that are normally not found in texts. For example, the theory of vector linear prediction is explained in considerable detail and so is the theory of line spectral processes. This focus and its small size make the book different from many excellent texts which cover the topic, including a few that are actually dedicated to linear prediction. There are several examples and computer-based demonstrations of the theory. Applications are mentioned wherever appropriate, but the focus is not on the detailed development of these applications. The writing style is meant to be suitable for self-study as well as for classroom use at the senior and first-year graduate levels. The text is self-contained for readers with introductory exposure to signal processing, random processes, and the theory of matrices, and a historical perspective and detailed outline are given in the first chapter. Table of Contents: Introduction / The Optimal Linear Prediction Problem / Levinson's Recursion / Lattice Structures for Linear Prediction / Autoregressive Modeling / Prediction Error Bound and Spectral Flatness / Line Spectral Processes / Linear Prediction Theory for Vector Processes / Appendix A: Linear Estimation of Random Variables / B: Proof of a Property of Autocorrelations / C: Stability of the Inverse Filter / Recursion Satisfied by AR Autocorrelations.
650 0 _aEngineering.
_99405
650 0 _aElectrical engineering.
_981421
650 0 _aSignal processing.
_94052
650 1 4 _aTechnology and Engineering.
_981422
650 2 4 _aElectrical and Electronic Engineering.
_981423
650 2 4 _aSignal, Speech and Image Processing.
_931566
710 2 _aSpringerLink (Online service)
_981424
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031013997
776 0 8 _iPrinted edition:
_z9783031036552
830 0 _aSynthesis Lectures on Signal Processing,
_x1932-1694
_981425
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02527-3
912 _aZDB-2-SXSC
942 _cEBK
999 _c85173
_d85173