Signal Detection In Non Gaussian Noise Environments

Signal Detection in Non-Gaussian Noise Environments

Adaptive Detection for Multichannel Signals in Non-Ideal Environments

This book systematically presents adaptive multichannel signal detection in three types of non-ideal environments, including sample-starved scenarios, signal mismatch scenarios, and noise plus subspace interference environments. The authors provide definitions of key concepts, detailed derivations of adaptive multichannel signal detectors, and specific examples for each non-ideal environment. In addition, the possible future trend of adaptive detection methods is discussed, as well as two further research points – namely, the adaptive detection algorithms based on information geometry, and the hybrid approaches that combine adaptive detection algorithms with machine learning algorithms. The book will be of interest to researchers, advanced undergraduates, and graduate students in sonar, radar signal processing, and communications engineering.

Advanced Theory of Signal Detection

We have some time ago noticed that finding a book dealing with topics in the ad vanced theory and applications of signal detection is not quite an easy matter. This is contrasted with that there are numerous books on the more general subject of de tection and estimation.Frankly, ourexperience and expertise is only on some partial portions of the theory and recent topics of signal detection. This book is therefore meant to include not all the advanced and interesting topics in the theory and appli cations ofsignal detection, butjust onlysome subsets of them: some such important and interesting topics and issues as distributed signal detection and sequential de tection are not considered only due to our limited knowledge and capacity. The goal we have in mind for this book is to present several advanced topics in signal detection theory and thereby help readers gain novel ideas and insights. In this book, we have tried to completely present in a unified way the theme oflocally optimum detection ofsignals in generalized observations. Among our hope is thus that the readers would be able to understand the concepts and fundamentals of a generalized observation model as applied to signal detection problems. This book will also allow the readers, whether they are students, academics, practitioners, or researchers, to have an expanded view on signal detection.