In this thesis the possibility for automatic classifications of radar
images has been evaluated. The topic automatic classification has always
been of interest in the radar community, and the improvements both of
hardware and software during the years today presents a new field for
engineers. This new field of classification opens up opportunities no one
would have believed in the early days of radar.
One possible approach classifying radar images is using radar range
profiles. Feature extraction of the peak amplitudes and their position in
the profiles is a common method. In this thesis, a classification of pure
range profiles without extraction of above mentioned features was evaluated.
The range profiles were obtained through software simulations. The software
was used to both model ground targets and simulating the range profiles.
Five target models were simulated, four were modeled and one already
existed in the software. All target models in the software were
simplifications of real targets. Real target simplifications are made
possible through the use of common geometrical figures assembled to form a
more complex, but still simplified target model. Common geometrical figures
used for modelling were: spheres, elliptical cylinders, rectangular plates
etc. The simulation software did not include any visual confirmation of the
modeled targets. A method using sinograms and the inverse radon transform
was developed. This method made it possible to visually confirm the modeled
targets and prevented incorrect placement or orientation of the geometrical
figures.
The selected classification algorithm was an artificial neural network. A
neural network is a mathematical simulation of the biological nervous
system. The core of a neural network consists of hidden neurons containing
threshold functions performing the calculations. The network implemented
was a standard 2-layer network, with non-linear threshold functions for the
neurons in the hidden layer and linear threshold functions for neurons in
the output layer. The range profiles were feed to the network as inputs and
the outputs were vectors with five values, one value for each target model.
The parameter settings of the neural network were evaluated. The neural
network classification algorithm was written in the MATLAB program
language.
The classification results revealed that it is possible to classify pure
range profiles with and without noise. The classification rate...
