| This
program provides instruction in basic principles and
practical techniques of particle filters, which are
being introduced in universities and laboratories throughout
the world. Filters are methods to obtain state estimation
from noisy observations, and one example of them is
the Kalamn filter, which has been used to estimate the
orbit of the spaceship in the Apollo mission. By approximating
a probability distribution using multiple particles,
particle filters have overcome limitations of the Kalman
filter, making it possible to use nonlinear non-Gaussian
state space models. Applied in a very broad range of
research, they are used in robots localization and recognition
of the surrounding environment, in visual tracking,
in intelligent sensing by fusion of voice, images and
other signals, in driver supports by estimating a driver's
intention, and so on. Come and challenge the issues,
which have never been solved, with particle filters
capable of describing this dynamically changing world
in broad and general. |
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