The purpose of this paper is to provide a practical introduction to the discrete Kalman
filter. This introduction includes a description and some discussion of the basic
discrete Kalman filter, a derivation, description and some discussion of the extended
Kalman filter, and a relatively simple (tangible) example with real numbers &
results.
In 1960, R.E. Kalman published his famous paper describing a recursive solution
to the discrete-data linear filtering problem. Since that time, due in large part to advances
in digital computing, the Kalman filter has been the subject of extensive research
and application, particularly in the area of autonomous or assisted
navigation.
In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata
linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital
computing, the
Kalman filter
has been the subject of extensive research and application,
particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the
general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete
introductory discussion can be found in [Sorenson70], which also contains some interesting
historical narrative.
his paper provides a tutorial and survey of methods for parameterizing
surfaces with a view to applications in geometric modelling and computer graphics.
We gather various concepts from di® erential geometry which are relevant to surface
mapping and use them to understand the strengths and weaknesses of the many
methods for parameterizing piecewise linear surfaces and their relationship to one
another.
In
this paper we propose to reduce the textural components by
modelling the coefficients of a wedgelet based regression tree
instead of the original pixel intensities
in this paper,wo propose an extension of the zerotree-based space-frequency quantization algorithm by adding a wedgelet symbol to its tree-pruning optimization.
in this paper,we show that an efficient multiscale wedgelet decomposition is possible if we carefully choose the set of possible wedgelet orientations.
This paper studies the problem of categorical data clustering,
especially for transactional data characterized by high
dimensionality and large volume. Starting from a heuristic method
of increasing the height-to-width ratio of the cluster histogram, we
develop a novel algorithm – CLOPE, which is very fast and
scalable, while being quite effective. We demonstrate the
performance of our algorithm on two real world
