Automatic classification of air tracks using raw video from a conventional surveillance radar
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Examensarbete för masterexamen
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Sammanfattning
Detecting the type of an airborne vehicle, such as an air plane or helicopter, spotted
by a radar, can be very time consuming and expensive if done by examining sensor
outputs. This might cause a problem if a potential threat has been detected since
the type, and thereafter identity, of a vehicle must be determined rapidly.
This thesis investigates the possibility of classifying jets and helicopters from raw
video, collected by a conventional surveillance radar, using a neural network. The
raw video is transformed from the time domain into the frequency domain using Fast
Fourier Transformation and then plotted as either spectrograms or range doppler
plots. In order to remove noise, the plots are filtered using the unsupervised algorithm
k-means clustering. Different neural networks such as convolutional neural
network (CNN), recurrent neural network (RNN) and convolutional recurrent neural
network (CRNN) are then trained and evaluated on the plots remaining after
filtering. The plots are treated as images by the network.
The results show that the CNN model trained on range doppler plots performs
the best with a test accuracy of 90% and a validation accuracy of 83%. Our conclusion
from this work is that it seems to be possible for a CNN to distinguish jets from
helicopters when it is fed with images of Fast Fourier Transformed raw video, but
some more research has to be done. Mainly, the model must be trained and tested
on more data. The contribution this thesis provides is not only that it seems to
be possible to classify jets and helicopters from raw video data in almost real time,
depending on the performance of the computer being used, but also that it seems
to be possible to receive a great accuracy when using a CNN for solving the task.
Beskrivning
Ämne/nyckelord
Computer, science, computer science, engineering, project, thesis, time series classification, CNN, RNN, k-means clustering