Particle Filters for Information Fusion Using Random Sets by Branko Ristic 0000-00-00 00:00:00

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by Branko Ristic
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Particle Filters for Information Fusion Using Random Sets by Branko Ristic
Author
Branko Ristic
Publisher
Springer
Date of release
Pages
150
ISBN
9781461463153
Binding
Hardcover
Illustrations
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PDF, EPUB, MOBI, TXT, DOC
Rating
3
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Book review

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.

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