Particle Filters for Information Fusion Using Random Sets
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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.
Format:Hardcover
Language:English
ISBN:1461463157
ISBN13:9781461463153
Release Date:April 2013
Publisher:Springer
Length:174 Pages
Weight:0.90 lbs.
Dimensions:0.7" x 6.1" x 9.2"
Related Subjects
Computers Computers & Technology Engineering Math Mathematics Science & Math TechnologyCustomer Reviews
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