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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.
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