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This book provides several efficient Kalman filters (linear or
nonlinear) under information theoretic criteria. They achieve
excellent performance in complicated non-Gaussian noises with low
computation complexity and have great practical application
potential. The book combines all these perspectives and results in
a single resource for students and practitioners in relevant
application fields. Each chapter starts with a brief review of
fundamentals, presents the material focused on the most important
properties and evaluates comparatively the models discussing free
parameters and their effect on the results. Proofs are provided at
the end of each chapter. The book is geared to senior
undergraduates with a basic understanding of linear algebra, signal
processing and statistics, as well as graduate students or
practitioners with experience in Kalman filtering.
This book presents the design, analysis, and application of
nonlinear adaptive filters with the goal of improving efficient
performance (ie the convergence speed, steady-state error, and
computational complexity). The authors present a nonlinear adaptive
filter, which is an important part of nonlinear system and digital
signal processing and can be applied to diverse fields such as
communications, control power system, radar sonar, etc. The authors
also present an efficient nonlinear filter model and robust
adaptive filtering algorithm based on the local cost function of
optimal criterion to overcome non-Gaussian noise interference. The
authors show how these achievements provide new theories and
methods for robust adaptive filtering of nonlinear and non-Gaussian
systems. The book is written for the scientist and engineer who are
not necessarily an expert in the specific nonlinear filtering field
but who want to learn about the current research and application.
The book is also written to accompany a graduate/PhD course in the
area of nonlinear system and adaptive signal processing.
Recently, criterion functions based on information theoretic
measures (entropy, mutual information, information divergence) have
attracted attention and become an emerging area of study in signal
processing and system identification domain. This book presents a
systematic framework for system identification and information
processing, investigating system identification from an information
theory point of view." "The book is divided into six chapters,
which cover the information needed to understand the theory and
application of system parameter identification. The authors
research provides a base for the book, but it incorporates the
results from the latest international research publications.
Named a 2013 Notable Computer Book for Information Systems by
"Computing Reviews"One of the first books to present system
parameter identification with information theoretic criteria so
readers can track the latest developmentsContains numerous
illustrative examples to help the reader grasp basic methods"
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