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.
General
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