1 Elements of Statistical Hypothesis Testing. - 1. 1 Introduction. - 1. 2 Basic Concepts of Hypothesis Testing. - 1. 3 Most Powerful Tests and the Neyman- Pearson Lemma. - 1. 4 Local Optimality and the Generalized Neyman-Pearson Lemma. - 1. 5 Bayes Tests. - 1. 6 A Characterization of the Relative Performance of Tests. - 2 Detection of Known Signals in Additive Noise. - 2. 1 Introduction. - 2. 2 The Observation Model. - 2. 3 Locally Optimum Detection and Asymptotic Optimality. - 2. 4 Detector Performance Comparisons. - 2. 5 Locally Optimum Bayes Detection. - 2. 6 Locally Optimum Multivariate Detectors. - Problems. - 3 Some Univariate Noise Probability Density Function Models. - 3. 1 Introduction. - 3. 2 Generalized Gaussian and Generalized Cauchy Noise. - 3. 3 Mixture Noise and Middleton Class A Noise. - 3. 4 Adaptive Detection. - Problems. - 4 Optimum Data Quantization in Known-Signal Detection. - 4. 1 Introduction. - 4. 2 Asymptotically Optimum Quantization. - 4. 3 Asymptotically Optimum Generalized Quantization. - 4. 4 Maximum-Distance Quantization. - 4. 5 Approximations of Locally Optimum Test Statistics. - Problems. - 5 Detection of Known Narrowband Signals in Narrowband Noise. - 5. 1 Introduction. - 5. 2 The Observation Model. - 5. 3 Locally Optimum Detection. - 5. 4 Asymptotic Performance Analysis. - 5. 5 Asymptotically Optimum Envelope Quantization. - 5. 6 Locally Optimum Bayes Detection. - Problems. - 6 Detection of Narrowband Signals with Random Phase Angles. - 6. 1 Introduction. - 6. 2 Detection of an Incoherent Signal. - 6. 3 Detection of a Noncoherent Pulse Train. - 6. 4 Asymptotically Optimum Quantization. - Problems. - 7 Detection of Random Signals in Additive Noise. - 7. 1 Introduction. - 7. 2 The Observation Model. - 7. 3 Locally Optimum Array Detection. - 7. 4 Asymptotic Performance Characteristics. - 7. 5 Asymptotically Optimum Quantization. - 7. 6 Detection of Narrowband Random Signals. - Problems. - References. Language: English