Signal Detection in Non-Gaussian Noise

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.