Algorithms for Statistical Signal Processing(絕)
- 20本以上，享 8.5折
售價
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洽詢
- 一般書籍
- ISBN：9780130622198
- 作者：John G. Proakis, Charles M. Rader, Fuyun Ling, Marc Moonen, Ian K. Proudler, Chrysostomos L. Nikias
- 版次：1
- 年份：2002
- 出版商：Pearson Education
- 頁數/規格：564頁
書籍介紹
DescriptionThe major focus of this book is on algorithms for statistical signal processing. Chapter 2 treats computationally efficient algorithms for convolution and for the computation of the discrete Fourier transform. Chapter 3 treats linear prediction and optimum Wiener filters; included in this chapter is a description of the Levinson-Durbin and Schur algorithms. Chapter 4 considers the filter design problem based on the least-squares method and describes several methods for solving least squares problems, including the Givens transformation, the Householder transformation, and singular-value decomposition. Chapter 5 treats single-channel adaptive filters based on the LMS algorithm and on recursive least-squares algorithms. Chapter 6 describes computationally efficient recursive least-squares algorithms for multichannel signals. Chapter 7 is focused on the uses of signal flow graphs for deriving computationally efficient adaptive filter algorithms based on the QR decomposition. Chapter 8 deals with power spectrum estimation, including both parametric and nonparametric methods. Chapter 9 describes the use of higher-order statistical methods for signal modeling and system identification.
Although the material in this book was written by six different authors, we have tried very hard to maintain common notation throughout the book. We believe we have succeeded in developing a coherent treatment of the major topics outlined in the preceding overview. Chapter 1 provides an introduction to selected basic DSP material that is typically found in a first-level DSP text, and also serves to establish some of the notation used throughout the book.
