将有关检测与估计的理论放在一本书中，使读者在学习过程中能够很好地把握两者之间的密切关系并关注那些容易混淆的问题。

为了能够用现代信号分析与处理方法讨论经典问题，本书在重点阐述离散信号处理的同时，通过介绍连续信号的表示来说明结论的一致性与连贯性。本书前半部分章节介绍检测与估计理论的基本概念，后半部分章节则通过若干专题说明检测与估计一般原理的具体应用，而这些应用专题只是大量应用问题中的一小部分，目的是为读者提供足够的相关知识，提高学习效率。

信号检测与估计是研究在噪声、干扰和信号共存的环境中如何正确发现、辨别和测量信号的技术，广泛应用于雷达和无线通信等领域。本书详细讲解了信号检测与估计的理论知识与实践应用，共分为四个部分。第一部分概述后续章节需要用到的基础知识，第二部分讲述检测理论的基础概念，包括二元假设检验、备择假设检验、具有随机参量的复合假设检验及非参量检验等。第三部分介绍单参数和多参数的估值方法以及波形估计理论。第四部分介绍某些理论的特定应用。全书共19章，各章都附有大量例题和习题，并给出数个MATLAB计算机仿真实验，以加深读者对于抽象理论的理解，便于读者实践掌握。

本书可作为高等院校通信类、信息类、电子类和控制类等专业的研究生或高年级本科生的专业教材，也可作为相关科研人员的参考用书。

Part Ⅰ Review Chapters

Chapter 1 Review of Probability

 1.1 Chapter Highlights

 1.2 Definition of Probability

 1.3 Conditional Probability

 1.4 Bayes' Theorem

 1.5 Independent Events

 1.6 Random Variables

 1.7 Conditional Distributions and Densities

 1.8 Functions of One Random Variable

 1.9 Moments of a Random Variable

 1.10 Distributions with Two Random Variables

 1.11 Multiple Random Variables

 1.12 Mean-Square Error (MSE) Estimation

 1.13 Bibliographical Notes

 1.14 Problems

Chapter 2 Stochastic Processes

 2.1 Chapter Highlights

 2.2 Stationary Processes

 2.3 Cyclostationary Processes

 2.4 Averages and Ergodicity

 2.5 Autocorrelation Function

 2.6 Power Spectral Density

 2.7 Discrete-Time Stochastic Processes

 2.8 Spatial Stochastic Processes

 2.9 Random Signals

 2.10 Bibliographical Notes

 2.11 Problems

Chapter 3 Signal Representations and Statistics

 3.1 Chapter Highlights

 3.2 Relationship of Power Spectral Density and Autocorrelation Function

 3.3 Sampling Theorem

 3.4 Linear Time-Invariant and Linear Shift-Invariant Systems

 3.5 Bandpass Signal Representations

 3.6 Bibliographical Notes

 3.7 Problems

Part Ⅱ Detection Chapters

Chapter 4 Single Sample Detection of Binary Hypotheses

 4.1 Chapter Highlights

 4.2 Hypothesis Testing and the MAP Criterion

 4.3 Bayes Criterion

 4.4 Minimax Criterion

 4.5 Neyman-Pearson Criterion

 4.6 Summary of Detection-Criterion Results Used in Chapter 4 Examples

 4.7 Sequential Detection

 4.8 Bibliographical Notes

 4.9 Problems

Chapter 5 Multiple Sample Detection of Binary Hypotheses

 5.1 Chapter Highlights

 5.2 Examples of Multiple Measurements

 5.3 Bayes Criterion

 5.4 Other Criteria

 5.5 The Optimum Digital Detector in Additive Gaussian Noise

 5.6 Filtering Alternatives

 5.7 Continuous Signals--White Gaussian Noise

 5.8 Continuous Signals--Colored Gaussian Noise

 5.9 Performance of Binary Receivers in AWGN

 5.10 Further Receiver-Structure Considerations

 5.11 Sequential Detection and Performance

 5.12 Bibliographical Notes

 5.13 Problems

Chapter 6 Detection of Signals with Random Parameters

 6.1 Chapter Highlights

 6.2 Composite Hypothesis Testing

 6.3 Unknown Phase

 6.4 Unknown Amplitude

 6.5 Unknown Frequency

 6.6 Unknown Time of Arrival

 6.7 Bibliographical Notes

 6.8 Problems

Chapter 7 Multiple Pulse Detection with Random Parameters

 7.1 Chapter Highlights

 7.2 Unknown Phase

 7.3 Unknown Phase and Amplitude

 7.4 Diversity Approaches and Performances

 7.5 Unknown Phase, Amplitude, and Frequency

 7.6 Bibliographical Notes

 7.7 Problems

Chapter 8 Detection of Multiple Hypotheses

 8.1 Chapter Highlights

 8.2 Bayes Criterion

 8.3 MAP Criterion

 8.4 M-ary Detection Using Other Criteria

 8.5 M-ary Decisions with Erasure

 8.6 Signal-Space Representations

 8.7 Performance of M-ary Detection Systems

 8.8 Sequential Detection of Multiple Hypotheses

 8.9 Bibliographical Notes

 8.10 Problems

Chapter 9 Nonparametric Detection

 9.1 Chapter Highlights

 9.2 Sign Tests

 9.3 Wilcoxon Tests

 9.4 Other Nonparametric Tests

 9.5 Bibliographical Notes

 9.6 Problems

Part Ⅲ Estimation Chapters

Chapter 10 Fundamentals of Estimation Theory

 10.1 Chapter Highlights

 10.2 Formulation of the General Parameter Estimation Problem

 10.3 Relationship between Detection and Estimation Theory

 10.4 Types of Estimation Problems

 10.5 Properties of Estimators

 10.6 Bayes Estimation

 10.7 Minimax Estimation

 10.8 Maximum-Likelihood Estimation

 10.9 Comparison of Estimators of Parameters

 10.10 Bibliographical Notes

 10.11 Problems

Chapter 11 Estimation of Specific Parameters

 11.1 Chapter Highlights

 11.2 Parameter Estimation in White Gaussian Noise

 11.3 Parameter Estimation in Nonwhite Gaussian Noise

 11.4 Amplitude Estimation in the Coherent Case with WGN

 11.5 Amplitude Estimation in the Noncoherent Case with WGN

 11.6 Phase Estimation in WGN

 11.7 Time-Delay Estimation in WGN

 11.8 Frequency Estimation in WGN

 11.9 Simultaneous Parameter Estimation in WGN

 11.10 Whittle Approximation

 11.11 Bibliographical Notes

 11.12 Problems

Chapter 12 Estimation of Multiple Parameters

 12.1 Chapter Highlights

 12.2 ML Estimation for a Discrete Linear Observation Model

 12.3 MAP Estimation for a Discrete Linear Observation Model

 12.4 Sequential Parameter Estimation

 12.5 Bibliographical References

 12.6 Problems

Chapter 13 Distribution-Free Estimation--Wiener Filters

 13.1 Chapter Highlights

 13.2 Orthogonality Principle

 13.3 Autoregressive Techniques

 13.4 Discrete Wiener Filter

 13.5 Continuous Wiener Filter

 13.6 Generalization of Discrete and Continuous Filter Representations

 13.7 Bibliographical Notes

 13.8 Problems

Chapter 14 Distribution-Free Estimation--Kalman Filter

 14.1 Chapter Highlights

 14.2 Linear Least-Squares Methods

 14.3 Minimum-Variance Weighted Least-Squares Methods

 14.4 Minimum-Variance Least-Squares or Kalman Algorithm

 14.5 Kalman Algorithm Computational Considerations

 14.6 Kalman Algorithm for Signal Estimation

 14.7 Continuous Kalman Filter

 14.8 Extended Kalman Filter

 14.9 Comments and Extensions

 14.10 Bibliographical Notes

 14.11 Problems

Part Ⅳ Application Chapters

Chapter 15 Detection and Estimation in Non-GaussianNoise Systems

 15.1 Chapter Highlights

 15.2 Characterization of Impulsive Noise

 15.3 Detector Structures in Non-Gaussian Noise

 15.4 Selected Examples of Noise Models, Receiver Structures, and Error-Rate Performance

 15.5 Estimation of Non-Gaussian Noise Parameters

 15.6 Bibliographical Notes

 15.7 Problems

Chapter 16 Direct-Sequence Spread-Spectrum Signals in Fading Multipath Channels

 16.1 Chapter Highlights

 16.2 Introduction to Direct-Sequence Spread Spectrum Communications

 16.3 Fading Multipath Channel Models

 16.4 Receiver Structures with Known Channel Parameters

 16.5 Receiver Structures without Knowledge of Phase

 16.6 Receiver Structures without Knowledge of Amplitude or Phase

 16.7 Receiver Structures and Performance with No Channel Knowledge

 16.8 Bibliographical Notes

 16.9 Problems

Chapter 17 Multiuser Detection

 17.1 Chapter Highlights

 17.2 Introduction

 17.3 Synchronous Multiuser Direct-Sequence CDMA

 17.4 Asynchronous Multiuser Direct-Sequence CDMA

 17.5 Speculative Summary

 17.6 Bibliographical Notes

 17.7 Problems

Chapter 18 Low-Probability-of-Intercept Communications

 18.1 Chapter Highlights

 18.2 LPI System Model

 18.3 Interceptor Detector Structures

 18.4 Filter-Bank Combiners

 18.5 Feature Detectors

 18.6 Bibliographical Notes

 18.7 Problems

Chapter 19 Spectrum Estimation

 19.1 Chapter Highlights

 19.2 Overview of Power Spectral Estimation

 19.3 Periodogram Techniques

 19.4 Parametric Spectral Estimation Techniques

 19.5 Examples of Spectral Estimation from MATLAB

 19.6 Bibliographical Notes

 19.7 Problems

Appendix A Properties of Distribution and Density Functions

Appendix B Common pdfs, cdfs, and Characteristic Functions

 B.1 One Point

 B.2 Zero-One

 B.3 Binomial

 B.4 Poisson

 B.5 Uniform

 B.6 Exponential

 B.7 Gaussian-Based Distributions

 B.8 Compilation of Mean, Variance, and Characteristic Function

Appendix C Multiple Normal Random Variables

 C.1 Zero-Mean Jointly Normal Real Random Variables

 C.2 Nonzero-Mean Jointly Normal Real Random Variables

 C.3 Linear Transformation of Zero-Mean Jointly Normal Real Random Variables

 C.4 Central Limit Theorem

 C.5 Nonzero Mean Jointly Normal Complex Random Variables

Appendix D Properties of Autocorrelation and Power Spectral Density Functions

 D.1 Autocorrelation Functions--Continuous Processes

 D.2 Power Spectral Density Functions--Continuous Process

 D.3 Properties of Discrete Processes

Appendix E Equivalence of LTI and LSI Bandlimited Systems

Appendix F Theory of Random Sums

Appendix G Evaluations Useful for Chapters 6, 7, and 16

Appendix H Gram-Charlier Type Series

Appendix I Mobile User Detection

 1.1 Overview of Commercial Cellular Networks

 1.2 CDMA

 1.3 Bibliographical Notes

Bibliography

Glossary

List of Symbols

Index