A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
Arulampalam, M.S.; Maskell, S.; Gordon, N.; Clapp, T.
Signal Processing, IEEE Transactions on
Volume 50, Issue 2, Feb 2002 Page(s):174 - 188
Digital Object Identifier 10.1109/78.978374
Summary:Increasingly, for many application areas, it is becoming important
to include elements of nonlinearity and non-Gaussianity in order to
model accurately the underlying dynamics of a physical system. Moreover,
it is typically crucial to process data on-line as it arrives, both from
the point of view of storage costs as well as for rapid adaptation to
changing signal characteristics. In this paper, we review both optimal
and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking
problems, with a focus on particle filters. Particle filters are
sequential Monte Carlo methods based on point mass (or "particle")
representations of probability densities, which can be applied to any
state-space model and which generalize the traditional Kalman filtering
methods. Several variants of the particle filter such as SIR, ASIR, and
RPF are introduced within a generic framework of the sequential
importance sampling (SIS) algorithm. These are discussed and compared
with the standard EKF through an illustrative example
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