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信號檢測與估計-理論與應用(英文版) 版權信息
- ISBN：9787121039249
- 條形碼：9787121039249 ; 978-7-121-03924-9
- 裝幀：暫無
- 冊數：暫無
- 重量：暫無
- 所屬分類：>>>
信號檢測與估計-理論與應用(英文版) 本書特色
信號檢測與估計是研究在噪聲、干擾和信號共存的環境中如何正確發現、辨別和測量信號的技術，廣泛應用于雷達和無線通信等領域。本書詳細講解了信號檢測與估計的理論知識與實踐應用，共分為四個部分。**部分概述后續章節需要用到的基礎知識，第二部分講述檢測理論的基礎概念，包括二元假設檢驗、備擇假設檢驗、具有隨機參量的復合假設檢驗及非參量檢驗等。第三部分介紹單參數和多參數的估值方法以及波形估計理論。第四部分介紹某些理論的特定應用。全書共19章，各章都附有大量例題和習題，并給出數個MATLAB計算機仿真實驗，以加深讀者對于抽象理論的理解，便于讀者實踐掌握。
本書可作為高等院校通信類、信息類、電子類和控制類等專業的研究生或高年級本科生的專業教材，也可作為相關科研人員的參考用書。
信號檢測與估計-理論與應用(英文版) 內容簡介
信號檢測與估計是研究在噪聲、干擾和信號共存的環境中如何正確發現、辨別和測量信號的技術，廣泛應用于雷達和無線通信等領域。本書詳細講解了信號檢測與估計的理論知識與實踐應用，共分為四個部分。**部分概述后續章節需要用到的基礎知識，第二部分講述檢測理論的基礎概念，包括二元假設檢驗、備擇假設檢驗、具有隨機參量的復合假設檢驗及非參量檢驗等。第三部分介紹單參數和多參數的估值方法以及波形估計理論。第四部分介紹某些理論的特定應用。全書共19章，各章都附有大量例題和習題，并給出數個MATLAB計算機仿真實驗，以加深讀者對于抽象理論的理解，便于讀者實踐掌握。
本書可作為高等院校通信類、信息類、電子類和控制類等專業的研究生或高年級本科生的專業教材，也可作為相關科研人員的參考用書。
信號檢測與估計-理論與應用(英文版) 目錄
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 Sspatial 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 Functi
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
……
Part Ⅲ Estimation Chapters
Part Ⅳ Application Chapters
Appendix A Properties of Distribution and Density Functions
Appendix B Common pdfs,cdfs, and characteristic Functions
Appendix C Multiple Normal Random Variables
Appendix D Properties of Autocorrelation and Power Spectral Density
Appendix E Equivalence of LTI and LSI Bandlimited Systems
Appendix F Theory of Random Sums
Appendix G Evaluations Useful for Chapters6,7,and 16
Appendix H Gram-Charlier Type Series
Appendix I Mobile User Detection
Bibliography
Glossary
List of Symbols
Index
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 Sspatial 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 Functi
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
……
Part Ⅲ Estimation Chapters
Part Ⅳ Application Chapters
Appendix A Properties of Distribution and Density Functions
Appendix B Common pdfs,cdfs, and characteristic Functions
Appendix C Multiple Normal Random Variables
Appendix D Properties of Autocorrelation and Power Spectral Density
Appendix E Equivalence of LTI and LSI Bandlimited Systems
Appendix F Theory of Random Sums
Appendix G Evaluations Useful for Chapters6,7,and 16
Appendix H Gram-Charlier Type Series
Appendix I Mobile User Detection
Bibliography
Glossary
List of Symbols
Index
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