PARTICLE FILTERING - Adaptive Signal Processing: Next Generation Solutions - page 271

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PARTICLE FILTERING
Petar M. Djuri´ and M´ nica F. Bugallo
Stony Brook University, Stony Brook, NY
Many problems in adaptive filtering are nonlinear and non-Gaussian. Of the many
methods that have been proposed in the literature for solving such problems, particle
filtering has become one of the most popular. In this chapter we provide the basics of
particle filtering and review its most important implementations. In Section 5.1 we
give a brief introduction to the area, and in Section 5.2 we motivate the use of particle
filtering by examples from several disciplines in science and engineering. We then
proceed with an introduction of the underlying idea of particle filtering and present
a detailed explanation of its essential steps (Section 5.3). In Section 5.4 we address
two important issues of particle filtering. Subsequently, in Section 5.5 we focus on
some implementations of particle filtering and compare their performance on syn-
thesized data. We continue by explaining the problem of estimating constant par-
ameters with particle filtering (Section 5.6). This topic deserves special attention
because constant parameters lack dynamics, which for particle filtering creates
some serious problems. We can improve the accuracy of particle filtering by a
method known as Rao-Blackwellization. It is basically a combination of Kalman
and particle filtering, and is discussed in Section 5.7. Prediction and smoothing
with particle filtering are described in Sections 5.8 and 5.9, respectively. In the last
two sections of the chapter, we discuss the problems of convergence of particle filters
(Section 5.10) and computational issues and hardware implementation of the filters
(Section 5.11). At the end of the chapter we present a collection of exercises which
should help the reader in getting additional insights into particle filtering.