Page 13 - Classification Parameter Estimation & State Estimation An Engg Approach Using MATLAB

2 INTRODUCTION

The sensory subsystem of a system produces measurement signals.
These signals carry the empirical knowledge. Often, the direct usage
of these signals is not possible, or inefficient. This can have several
causes:

. The information in the signals is not represented in an explicit way.
It is often hidden and only available in an indirect, encoded form.
. Measurement signals always come with noise and other hard-
to-predict disturbances.
. The information brought forth by posterior knowledge is more
accurate and more complete than information brought forth by
empirical knowledge alone. Hence, measurement signals should
be used in combination with prior knowledge.

Measurement signals need processing in order to suppress the noise and
to disclose the information required for the task at hand.

1.1 THE SCOPE OF THE BOOK

In a sense, classification and estimation deal with the same pro-
blem: given the measurement signals from the environment, how
can the information that is needed for a system to operate in the
real world be inferred? In other words, how should the measure-
ments from a sensory system be processed in order to bring max-
imal information in an explicit and usable form? This is the main
topic of this book.
Good processing of the measurement signals is possible only if
some knowledge and understanding of the environment and the
sensory system is present. Modelling certain aspects of that environ-
ment – like objects, physical processes or events – is a necessary task
for the engineer. However, straightforward modelling is not always
possible. Although the physical sciences provide ever deeper insight
into nature, some systems are still only partially understood; just
think of the weather. But even if systems are well understood,
modelling them exhaustively may be beyond our current capabilities
(i.e. computer power) or beyond the scope of the application. In such
cases, approximate general models, but adapted to the system at
hand, can be applied. The development of such models is also a
topic of this book.