Vehicle Classification Using Neural Network in Forward Scattering Radar
This thesis unveils the potential and utilization of Neural Network (NN) in radar applications for target classification. The radar system under test is a special of its kind and known as Forward Scattering Radar (FSR). FSR is a special type of bistatic radar which the transmitted energy is scatt...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English |
| Published: |
2009
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| Online Access: | http://psasir.upm.edu.my/id/eprint/7339/1/FK_2009_21a.pdf http://psasir.upm.edu.my/id/eprint/7339/ |
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| Summary: | This thesis unveils the potential and utilization of Neural Network (NN) in radar
applications for target classification. The radar system under test is a special of its kind
and known as Forward Scattering Radar (FSR). FSR is a special type of bistatic radar
which the transmitted energy is scattered by a target and the target is so close to the
transmitter-receiver baseline. Recent works had shown that FSR can be effectively used
for classification, but the result can be further improved by using advance classification
method. To proof this, result from FSR experiment were used. The target used for this
experiment is a ground vehicle which is represented by typical public road transport.
New features from raw radar signal were determined and extracted manually prior to
classification process using Neural Network (NN). Two types of features in the time and
frequency domain signature were examined, namely time required for counting zero
crossings, first main lobe width, second main lobe- width and the number of lobes.
Multilayer perceptron (MLP) back-propagation neural network trained with back propagation algorithm was implemented and analyzed. In NN classifier, the unknown
target is sent to the network trained by the known targets to attain the accurate output.
Two tasks of classifications are analyzed. The first task is to recognize the exact type of
vehicle, four vehicle types were selected: Vauxhall Astra, Renault Traffic, Vauxhall
Combo and Honda Civic. The second task is to group vehicle into their categories:
small, medium and large. The proposed NN provides high percentage of successful
classification which is 90% and 98% of overall data was correctly classified in vehicle
recognition and vehicle categorisation respectively. The result presented show that NN
can be effectively employed in FSR system as a classification method. |
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