Adaptive Signal Processing: Next Generation Solutions

Leading experts present the latest research results in adaptive signal processing
Recent developments in signal processing have made it clear that significant performance gains can be achieved beyond those achievable using standard adaptive filtering approaches. Adaptive Signal Processing presents the next generation of algorithms that will produce these desired results, with an emphasis on important applications and theoretical advancements. This highly unique resource brings together leading authorities in the field writing on the key topics of significance, each at the cutting edge of its own area of specialty. It begins by addressing the problem of optimization in the complex domain, fully developing a framework that enables taking full advantage of the power of complex–valued processing. Then, the challenges of multichannel processing of complex–valued signals are explored. This comprehensive volume goes on to cover Turbo processing, tracking in the subspace domain, nonlinear sequential state estimation, and speech–bandwidth extension.
Examines the seven most important topics in adaptive filtering that will define the next–generation adaptive filtering solutions
Introduces the powerful adaptive signal processing methods developed within the last ten years to account for the characteristics of real–life data: non–Gaussianity, non–circularity, non–stationarity, and non–linearity
Features self–contained chapters, numerous examples to clarify concepts, and end–of–chapter problems to reinforce understanding of the material
Contains contributions from acknowledged leaders in the field
Includes a Solutions Manual for instructors
Adaptive Signal Processing is an invaluable tool for graduate students, researchers, and practitioners working in the areas of signal processing, communications, controls, radar, sonar, and bio medical engineering.

Spis treści:
Chapter 1: Complex–valued adaptive signal processing (Tulay Adali and Hualiang Li).
Chapter 2: Robust estimation techniques for complex–valued random vectors (Esa Ollila and Visa Koivunen).
Chapter 3: Turbo equalization (Phillip A. Regalia).
Chapter 4: Subspace tracking (Jean–Pierre Delmas).
Chapter 5: Particle filters (Petar Djuric and Monica Bugallo).
Chapter 6: Patter Recognition using Sequential State Estimation (Simon Haykin and Ienkaran Arasaratnam).
Chapter 7: Bandwidth extension of telephony speech (Bernd Iser and Gerhard Schmidt).

Nota biograficzna:

TÜLay?Adali, PhD, is Professor of Electrical Engineering and Director of the Machine Learning for Signal Processing Laboratory at the University of Maryland, Baltimore County. Her research interests are in statistical and adaptive signal processing, with emphasis on nonlinear and complex–valued signal processing, and applications in biomedical data analysis and communications.
Simon Haykin, PhD, is Distinguished University Professor and Director of the Cognitive Systems Laboratory in the Faculty of Engineering at McMaster University. A world–renowned authority on adaptive and learning systems, Dr. Haykin has pioneered signal–processing techniques and systems for radar and communication applications, culminating in the study of cognitive dynamic systems, which has become his research passion.

Okładka tylna:

Leading experts present the latest research results in adaptive signal processing
Recent developments in signal processing have made it clear that significant performance gains can be achieved beyond those achievable using standard adaptive filtering approaches. Adaptive Signal Processing presents the next generation of algorithms that will produce these desired results, with an emphasis on important applications and theoretical advancements. T his highly unique resource brings together leading authorities in the field writing on the key topics of significance, each at the cutting edge of its own area of specialty. It begins by addressing the problem of optimization in the complex domain, fully developing a framework that enables taking full advantage of the power of complex–valued processing. Then, the challenges of multichannel processing of complex–valued signals are explored. This comprehensive volume goes on to cover Turbo processing, tracking in the subspace domain, nonlinear sequential state estimation, and speech–bandwidth extension.
Examines the seven most important topics in adaptive filtering that will define the next–generation adaptive filtering solutions
Introduces the powerful adaptive signal processing methods developed within the last ten years to account for the characteristics of real–life data: non–Gaussianity, non–circularity, non–stationarity, and non–linearity
Features self–contained chapters, numerous examples to clarify concepts, and end–of–chapter problems to reinforce understanding of the material
Contains contributions from acknowledged leaders in the field
Includes a Solutions Manual for instructors
Adaptive Signal Processing is an invaluable tool for graduate students, researchers, and practitioners working in the areas of signal processing, communications, controls, radar, sonar, and biomedical engineering.