國外電子信息類係列教材：統計與自適應信號處理（英文改編版） pdf epub mobi txt 電子書 下載 2025

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[美] Dimitris G.Manolakis，[美] Vinay K.Ingle，[美] Stephen M.Kogon 著

齣版社： 西安電子科技大學齣版社

ISBN：9787560628486

版次：1

商品編碼：11147613

包裝：平裝

開本：16開

齣版時間：2012-08-01

用紙：膠版紙

頁數：382

編輯推薦

　　《國外電子信息類係列教材：統計與自適應信號處理（英文改編版）》由Dimitris G.Manolakis、Vinay K.Ingle、Stephen M.Kogon著，闊永江改編，內容：Chapter 1 introduces the basic concepts and applications of statistical and adaptive signal processing and provides an overview of the book.Chapters 2 introduce some basic concepts of estimation theory.Chapter 3 provides a treatment of parametric linear signal models in the time and frequency domains.Chapter 4 presents the most practical methods for the estimation of correlation and spectral densities.Chapter 5 provides a detailed study of the theoretical properties of optimum filters，assuming that the relevant signals can be modeled as stochastic processes with known statistical properties; and Chapter 6 contains algorithms and structures for optimum filtering，signal modeling，and prediction.Chapter 7 introduces the principle of least-squares estimation and its application to the design of practical filters and predictors……

內容簡介

　　《國外電子信息類係列教材：統計與自適應信號處理（英文改編版）》介紹瞭統計與自 適應信號處理的基本概念和應用，包括隨機序列分析、譜估計以及自適應濾波等內容。本書可作為電子、通信、自動化、電機、生物醫 學和機械工程等專業研究生作為教材或教學參考書，也可作為廣大工程技術人員的自學讀本或參考用書。

作者簡介

　　Dimitris G.Manolakis：於希臘雅典大學獲得物理學士學位和電氣工程博士學位，現任美國麻省林肯實驗室研究員；曾在Riveride研究所任主任研究員，並曾在雅典大學、美國東北大學、波士頓學院、沃切斯特理工學院任教。
　　Vinay K.Ingle：於倫斯勒理工學院獲得電氣和計算機工程的博士學位，曾在多所大學講授過信號處理課程，具有豐富的研究經曆；1981年加入美國東北大學，目前在電氣工程和計算機係任職。
　　Stephen M.Kogon：於佐治亞理工學院獲得電氣工程博士學位，現任美國麻省林肯實驗室研究員；曾就職於Raytheon公司、波士頓大學和佐治亞技術研究所。

目錄

CHAPTER 1 Introduction
1.1 Random Signals
1.2 Spectral Estimation
1.3 Signal Modeling
1.4 Adaptive Filtering
1.4.1 Applicatior of Adaptive Filter
1.4.2 Features of Adaptive Filter
1.5 Organization of the Book
CHAPTER 2 Random Sequences
2.1 Discrete-Time Stochastic Processes
2.1.1 Description Using Probability Functior
2.1.2 Second-Order Statistical Description
2.1.3 Stationarity
2.1.4 Ergodicity
2.1.5 Random Signal Variability
2.1.6 Frequency-Domain Description of Stationary Processes
2.2 Linear Systems with Stationary Random Inputs
2.2.1 Time-Domain Analysis
2.2.2 Frequency-Domain Analysis
2.2.3 Random Signal Memory
2.2.4 General Correlation Matrices
2.2.5 Correlation Matrices from Random Processes
2.3 Innovatior Representation of Random Vector
2.4 Principles of Estimation Theory
2.4.1 Properties of Estimator
2.4.2 Estimation of Mean
2.4.3 Estimation of Variance
2.5 Summary
Problems
CHAPTER 3 Linear Signal Models
3.1 Introduction
3.1.1 Linear Nonparametric Signal Models
3.1.2 Parametric Pole-Zero Signal Models
3.1.3 Mixed Processes and Wold Decomposition
3.2 All-Pole Models
3.2.1 Model Properties
3.2.2 All-Pole Modeling and Linear Prediction
3.2.3 Autoregressive Models
3.2.4 Lower-Order Models
3.3 All-Zero Models
3.3.1 Model Properties
3.3.2 Moving-Average Models
3.3.3 Lower-Order Models
3.4 Pole-Zero Models
3.4.1 Model Properties
3.4.2 Autoregressive Moving-Average Models
3.4.3 The Firt-Order Pole-Zero Model：PZ（1，1）
3.4.4 Summary and Dualities
3.5 Summary
Problems
CHAPTER 4 Nonparametric Power Spectrum Estimation
4.1 Spectral Analysis of Deterministic Signals
4.1.1 Effect of Signal Sampling
4.1.2 Windowing，Periodic Exterion，and Extrapolation
4.1.3 Effect of Spectrum Sampling
4.1.4 Effects of Windowing：Leakage and Loss of Resolution
4.1.5 Summary
4.2 Estimation of the Autocorrelation of Stationary Random Signals
4.3 Estimation of the Power Spectrum of Stationary Random Signals
4.3.1 Power Spectrum Estimation Using the Periodogram
4.3.2 Power Spectrum Estimation by Smoothing a Single Periodogram——The Blackman-Tukey Method
4.3.3 Power Spectrum Estimation by Averaging Multiple Periodograms——The Welch-Bartlett Method
4.3.4 Some Practical Corideratior and Examples
4.4 Multitaper Power Spectrum Estimation
4.5 Summary
Problems
CHAPTER 5 Optimum Linear Filter
5.1 Optimum Signal Estimation
5.2 Linear Mean Square Error Estimation
5.2.1 Error Performance Surface
5.2.2 Derivation of the Linear MMSE Estimator
5.2.3 Principal-Component Analysis of the Optimum Linear Estimator
5.2.4 Geometric Interpretatior and the Principle of Orthogonality
5.2.5 Summary and Further Properties
5.3 Optimum Finite Impulse Respore Filter
5.3.1 Design and Properties
5.3.2 Optimum FIR Filter for Stationary Processes
5.3.3 Frequency-Domain Interpretatior
5.4 Linear Prediction
5.4.1 Linear Signal Estimation
5.4.2 Forward Linear Prediction
5.4.3 Backward Linear Prediction
5.4.4 Stationary Processes
5.4.5 Properties
5.5 Optimum Infinite Impulse Respore Filter
5.5.1 Noncausal IIR Filter
5.5.2 Causal IIR Filter
5.5.3 Filtering of Additive Noise
5.5.4 Linear Prediction Using the Infinite Past——Whitening
5.6 Invere Filtering and Deconvolution
5.7 Summary
Problems
CHAPTER 6 Algorthms and Structures for Optimum Linear Filter
6.1 Fundamentals of Order-Recurive Algorithms
6.1.1 Matrix Partitioning and Optimum Nesting .
6.1.2 Inverion of Partitioned Hermitian Matrices
6.1.3 Leviron Recurion for the Optimum Estimator
6.1.4 Order-Recurive Computation of the LDLH Decomposition
6.1.5 Order-Recurive Computation of the Optimum Estimate
6.2 Interpretatior of Algorithmic Quantities
6.2.1 Innovatior and Backward Prediction
6.2.2 Partial Correlation
6.2.3 Order Decomposition of the Optimum Estimate
6.2.4 Gram-Schmidt Orthogonalization
6.3 Order-Recurive Algorithms for Optimum FIR Filter
6.3.1 Order-Recurive Computation of the Optimum Filter
6.3.2 Lattice-Ladder Structure
6.3.3 Simplificatior for Stationary Stochastic Processes
6.4 Algorithms of Leviron and Leviron-Durbin
6.5 Lattice Structures for Optimum Fir Filter And Predictor
6.5.1 Lattice-Ladder Structures
6.5.2 Some Properties and Interpretatior
6.5.3 Parameter Converior
6.6 Summary
Problems
CHAPTER 7 Least-Squares Filtering and Prediction
7.1 The Principle of Least Squares
7.2 Linear Least-Squares Error Estimation
7.2.1 Derivation of the Normal Equatior
7.2.2 Statistical Properties of Least-Squares Estimater
7.3 Least-Squares FIR Filter
7.4 Linear Least-Squares Signal Estimation
7.4.1 Signal Estimation and Linear Prediction
7.4.2 Combined Forward and Backward Linear Prediction（FBLP）
7.4.3 Narrowband Interference Cancelation
7.5 LS Computatior Using the Normal Equatior
7.5.1 Linear LSE Estimation
7.5.2 LSE FIR Filtering and Prediction
7.6 Summary
Problems
CHAPTER 8 Signal Modeling and Parametric Spectral Estimation
8.1 The Modeling Process：Theory and Practice
8.2 Estimation of All-Pole Models
8.2.1 Direct Structures
8.2.2 Lattice Structures
8.2.3 Maximum Entropy Method
8.2.4 Excitatior with Line Spectra
8.3 Estimation Of Pole-Zero Models
8.3.1 Known Excitation
8.3.2 Unknown Excitation
8.4 Applicatior
8.4.1 Spectral Estimation
8.4.2 Speech Modeling
8.5 Harmonic Models and Frequency Estimation Techniques
8.5.1 Harmonic Model
8.5.2 Pisarenko Harmonic Decomposition
8.5.3 MUSIC Algorithm
8.5.4 Minimum-Norm Method
8.5.5 ESPRIT Algorithm
8.6 Summary
Problems
CHAPTER 9 Adaptive Filter
9.1 Typical Applicatior of Adaptive Filter
9.1.1 Echo Cancelation in Communicatior
9.1.2 Linear Predictive Coding
9.1.3 Noise Cancelation
9.2 Principles of Adaptive Filter
9.2.1 Features of Adaptive Filter
9.2.2 Optimum verus Adaptive Filter
9.2.3 Stability and Steady-State Performance of Adaptive Filter
9.2.4 Some Practical Corideratior
9.3 Method of Steepest Descent
9.4 Least-Mean-Square Adaptive Filter
9.4.1 Derivation
9.4.2 Adaptation in a Stationary SOE
9.4.3 Summary and Design Guidelines
9.4.4 Applicatior of the LMS Algorithm
9.4.5 Some Practical Corideratior
9.5 Recurive Least-Squares Adaptive Filter
9.5.1 LS Adaptive Filter
9.5.2 Conventional Recurive Least-Squares Algorithm
9.5.3 Some Practical Corideratior
9.5.4 Convergence and Performance Analysis
9.6 Fast RLS Algorithms for FIR Filtering
9.6.1 Fast Fixed-Order RLS FIR Filter
9.6.2 RLS Lattice-Ladder Filter
9.6.3 RLS Lattice-Ladder Filter Using Error Feedback Updatings
9.7 Tracking Performance of Adaptive Algorithms
9.7.1 Approaches for Nortationary SOE
9.7.2 Preliminaries in Performance Analysis
9.7.3 LMS Algorithm
9.7.4 RLS Algorithm with Exponential Forgetting
9.7.5 Comparison of Tracking Performance
9.8 Summary
Problems

前言/序言

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評分☆☆☆☆☆

算法經典，性能優越。

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