Classification of targets is a key problem of modern radar and sonar systems.
This is an activity carried out with great success by echolocating mammals, such as bats, that have evolved echolocation as a means of detecting, selecting
and attacking prey over a period of more than 50 million years. Because they have developed a highly sophisticated capability on which they depend for
their survival, it is likely that there is potentially a great deal that can be
learnt from understanding how they use this capability and how this might
be valuably applied to radar and sonar systems. Bat-pollinated plants and
their flowers represent a very interesting class of organisms for the study of target classification as it is thought that co-evolution has shaped bat-pollinated
flowers in order to ease classification by bats. In this thesis, the
strategy that underpins classification of
flowers by bats is investigated. An
acoustic radar has been developed to collect data to perform a floral echoes
analysis. Results show that there is a relative relevance of specific parts of the
flower in displaying information to bats and show that there are different characteristics in the
flowers' echo fingerprints, depending on age and stage
of maturity, that bats might use to choose the most suitable flowers for
pollination. We show that, as suggested by the
oral echoes analysis, a
more intelligent way to perform target classification can result in improved
classification performance and, investigate biologically inspired methods and
ideas that might become important tools for the study and the development
of radar and sonar target classification