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Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: 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 Formul. Bookseller Inventory #

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This is the first reader-friendly book to comprehensively address the topics of both detection and estimation – with a thorough discussion of the underlying theory as well as the practical applications.   Modernizes classical topics by focusing on discrete signal processing with continuous signal presentations included to demonstrate uniformity and consistency of the results. Summarizes concepts that are extensively treated in other sources, but are provided here to reacquaint readers with these topics and introduce a consistent notation used throughout. Illustrates the application of previously developed general principles. CoversMATLAB m-file and Simulink routines;does not require prior knowledge of MATLAB. A useful reference text for practicing engineers.

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