Ebook: Signal Detection in Non-Gaussian Noise

This book contains a unified treatment of a class of problems of signal detection theory. This is the detection of signals in addi­ tive noise which is not required to have Gaussian probability den­ sity functions in its statistical description. For the most part the material developed here can be classified as belonging to the gen­ eral body of results of parametric theory. Thus the probability density functions of the observations are assumed to be known, at least to within a finite number of unknown parameters in a known functional form. Of course the focus is on noise which is not Gaussian; results for Gaussian noise in the problems treated here become special cases. The contents also form a bridge between the classical results of signal detection in Gaussian noise and those of nonparametric and robust signal detection, which are not con­ sidered in this book. Three canonical problems of signal detection in additive noise are covered here. These allow between them formulation of a range of specific detection problems arising in applications such as radar and sonar, binary signaling, and pattern recognition and classification. The simplest to state and perhaps the most widely studied of all is the problem of detecting a completely known deterministic signal in noise. Also considered here is the detection random non-deterministic signal in noise. Both of these situa­ of a tions may arise for observation processes of the low-pass type and also for processes of the band-pass type.

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This book contains a unified treatment of a class of problems of signal detection theory. This is the detection of signals in additive noise which is not required to have Gaussian probability density functions in its statistical description. For the most part the material developed here can be classified as belonging to the general body of results of parametric theory. Thus the probability density functions of the observations are assumed to be known, at least within a finite number of unknown parameters in a known functional form. Of course the focus is on noise which is not Gaussian; results for Gaussian noise in the problems treated here become special cases. The contents also form a bridge between the classical results of signal detection in Gaussian noise and those of nonparametric and robust signal detection, which are not considered in this book.

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This book contains a unified treatment of a class of problems of signal detection theory. This is the detection of signals in additive noise which is not required to have Gaussian probability density functions in its statistical description. For the most part the material developed here can be classified as belonging to the general body of results of parametric theory. Thus the probability density functions of the observations are assumed to be known, at least within a finite number of unknown parameters in a known functional form. Of course the focus is on noise which is not Gaussian; results for Gaussian noise in the problems treated here become special cases. The contents also form a bridge between the classical results of signal detection in Gaussian noise and those of nonparametric and robust signal detection, which are not considered in this book.
Content:
Front Matter....Pages i-ix
Elements of Statistical Hypothesis Testing....Pages 1-23
Detection of Known Signals in Additive Noise....Pages 24-70
Some Univariate Noise Probability Density Function Models....Pages 72-96
Optimun Data Quantization in Known-Signal Detection....Pages 97-126
Detection of known Narrowband Signals in Narrowband Noise....Pages 127-150
Detection of Narrowband Signals with Random Phase Angles....Pages 151-184
Detection of Random Signals in Additive Noise....Pages 185-214
Back Matter....Pages 215-234

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This book contains a unified treatment of a class of problems of signal detection theory. This is the detection of signals in additive noise which is not required to have Gaussian probability density functions in its statistical description. For the most part the material developed here can be classified as belonging to the general body of results of parametric theory. Thus the probability density functions of the observations are assumed to be known, at least within a finite number of unknown parameters in a known functional form. Of course the focus is on noise which is not Gaussian; results for Gaussian noise in the problems treated here become special cases. The contents also form a bridge between the classical results of signal detection in Gaussian noise and those of nonparametric and robust signal detection, which are not considered in this book.
Content:
Front Matter....Pages i-ix
Elements of Statistical Hypothesis Testing....Pages 1-23
Detection of Known Signals in Additive Noise....Pages 24-70
Some Univariate Noise Probability Density Function Models....Pages 72-96
Optimun Data Quantization in Known-Signal Detection....Pages 97-126
Detection of known Narrowband Signals in Narrowband Noise....Pages 127-150
Detection of Narrowband Signals with Random Phase Angles....Pages 151-184
Detection of Random Signals in Additive Noise....Pages 185-214
Back Matter....Pages 215-234
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