檢測、估值與調(diào)制理論

內(nèi)容概要

本書是一本非常經(jīng)典的詳細(xì)介紹檢測、估值與調(diào)制理論的教學(xué)參考書，在美國自1968年首次出版以來，已重印28次。主要內(nèi)容包括隨機(jī)過程的表示、檢測與估值的分類、信號檢測、信號參數(shù)的估計、連續(xù)波形的估計、線性估計和線性調(diào)制理論等。    本書可用做通信與電子、信息與信號處理等專業(yè)的高年級本科生、研究生教學(xué)參考書。

書籍目錄

1 Introduction　1.1 Topical Outline　1.2 Possible Approaches　1.3 Organization2 Classical Detection and Estimation Theory　2.1 Introduction　2.2 Simple Binary Hypothesis Tests　　Decision Criteria.Performance:Receiver Operating Characteristic　2.3 M Hypotheses　2.4 Estimation Theory　　Random Parameters:BayesEstimation.Real(Nonrandom)Parameter Estimation.Multiple Parameter Estimation.Summary of Estimation Theory.　2.5 Composite Hypotheses　2.6 The General Gaussian Problem　　Equal Covariance Matrices.Equal Mean Vectors.Summary.　2.7 Performance Bounds and Approximations　2.8 Summary　2.9 Problems　Referecnces3 Representations of Random Processes　3.1 Introduction　3.2 Deterministic Functions:Orthogonal Representations　3.3 Random Process CharacterizationRandom Processes:Conventional Characterizations.Series Representation of Sample Functions of Random Processes.Gaussian Processes.　3.4 Homogeneous Integral Equations and Eigenfunctions　　Rational Spectra.Bandlimited Spectra.Nonstationary Processes.White Noise Processes.The Optimum Linear Filler.Properties of Eigenfunctions and Eigenvalues.　3.5 Periodic Processes　3.6 Infinite Time Interval:Spectral Decomposition　　Spectral Decomposition.An Application of Spectral Decomposition:MAP Estimation of a Gaussian Process.　3.7 Vector Random Processes　3.8 Summary　3.9 Problems　Referecnces4 Detection of Signals-Estimation of Signal Parameters　4.1 Introduction　　Models.Format.　4.2 Detection and Estimation in White Gaussian Noise　　Detection of Signals in Additive White Gaussian Noise.Linear Estimation.Nonlinear Estimation.Summary:Known Signals in White Gaussian Noise　4.3 Detection and Estimation in Nonwhite Gaussian Noise　　“Whitening”Approach.A Direct Derivation Using the Karhunen-Loeve Expansion.A Direct Derivation Using the Suffcient Statistic.Detection Performance.Estimation.Solution Techniques for Integral Equations.Sensitivity.Known Linear Channels　4.4 Signals with Unwanted Parameters:The Composite Hypothesis Problem　　Random Phase angles.Random Amplitude and Phase.　4.5 Multiple Channels　　Formulation.Application.　4.6 Multiple Parameter Estimation　　Additive White Gaussian Noise Channel.Extensions.　4.7 Summary and Omissions　　Summary.Topics Omitted.　4.8 Problems　Referecnces5 Estimation of Continuous Waveforms　5.1 Introduction　5.2 Derivation of Estimator Equations　　No-Memory Modulation Systems.Modulation Systems with Memory.　5.3 A Lower Bound on the Mean-Square Estimation Error　5.4 Multidimensional Waveform Estimation　　Examples of Multidimensional Problems.Problem Formulation.Derivation of Estimator Equations.Lower Bound on the Error Matrix.Colored Noise Estimation.　5.5 Nonrandom Waveform Estimation　5.6 Summary　5.7 Problems　Referecnces6 Linear Estimation　6.1 Properties of Optimum Processors　6.2 Realizable Linear Filters:Stationary 　　Processes,Infinite Past:Wiener Filterssolution of Wiener-Hopf Equation.Errors in Optimum Systems.Unrealizable Filters.Closed-Form Error Expressions.Optimum Feedback Systems.Comments.　6.3 Kalman-Bucy Filters　　Differential Equation Representation of Linear Systems and Random Process Generation.Derivation of Estimator Equations.Applications.Generalizations.　6.4 Linear Modulation:Communications Context　　DSB-AM:Realizable Demodulation.DSB-AM:Demodulation with delay.Amplitude Modulation:Generalized Carriers.Amplitude Modulation:Single-Sideband Suppressed-Carrier.　6.5 The Fundamental Role of the Optimum Linear Filter　6.6 Comments　6.7 Problems　Referecnces7 Discussion　7.1 Summary　7.2 Preview of Part II　7.3 Unexplored Issues　ReferencesAppendix:A Typical Course OutlineGlossaryAuthor IndexSubject Index

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