Page 22 - Classification Parameter Estimation & State Estimation An Engg Approach Using MATLAB

THE ORGANIZATION OF THE BOOK 11

. Knowledge about various methods to fulfil the processing tasks of
the instrument. This is needed in order to generate a number of
different design concepts.
. Knowledge about how to evaluate the various methods. This is
needed in order to select the best design concept.
. A tool for the experimental evaluation of the design concepts.

The book does not address the topic ‘sensor technology’. For this, many
good textbooks already exist, for instance see Regtien et al. (2004) and
Brignell and White (1996). Nevertheless, the sensory system does have a
large impact on the required processing. For our purpose, it suffices to
consider the sensory subsystem at an abstract functional level such that it
can be described by a mathematical model.

1.3 THE ORGANIZATION OF THE BOOK

The first part of the book, containing Chapters 2, 3 and 4, considers each of
the three topics – classification, parameter estimation and state estimation –
at a theoretical level. Assuming that appropriate models of the objects,
physical process or events, and of the sensory system are available, these
three tasks are well defined and can be discussed rigorously. This facilitates
the development of a mathematical theory for these topics.
The second part of the book, Chapters 5 to 8, discusses all kinds of
issues related to the deployment of the theory. As mentioned in Section
1.1, a key issue is modelling. Empirical data should be combined with
prior knowledge about the physical process underlying the problem at
hand, and about the sensory system used. For classification problems,
the empirical data is often represented by labelled training and evalua-
tion sets, i.e. sets consisting of measurement vectors of objects together
with the true classes to which these objects belong. Chapters 5 and 6
discuss several methods to deal with these sets. Some of these techni-
ques – probability density estimation, statistical inference, data fitting –
are also applicable to modelling in parameter estimation. Chapter 7 is
devoted to unlabelled training sets. The purpose is to find structures
underlying these sets that explain the data in a statistical sense. This is
useful for both classification and parameter estimation problems. The
practical aspects related to state estimation are considered in Chapter 8.
In the last chapter all the topics are applied in some fully worked out
examples. Four appendices are added in order to refresh the required
mathematical background knowledge.