Particle Filters for Random Set Models

Ristic, B 2013, Particle Filters for Random Set Models, Springer, New York, United States.

Document type: Book
Collection: Books

Title Particle Filters for Random Set Models
Author(s) Ristic, B
Year 2013
Publisher Springer
Place of publication New York, United States
Subjects Signal Processing
Summary This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. Although the resulting algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.
Copyright notice © Springer Science+Business Media New York 2013
DOI - identifier 10.1007/978-1-4614-6316-0
ISBN 9781461463153
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