Automatic target recognition of quadcopter type drones from moderately-wideband electromagnetic data using convolutional neural networks

Güneri, Rutkay
In this thesis, the classifier design approach based on “Learning by a Convolutional Neural Network (CNN)” will be applied to two different target library/data sets; an ultra-wideband simulation data (from 37 MHz to 19.1 GHz) obtained for a target library of four dielectric spheres, and a moderately-wide band measurement data (from 3.1 to 4.8 GHz) obtained for a target library of four quadcopter type unmanned aerial vehicles (UAVs). While the bandwidth of simulation data for spherical targets is about nine octaves, the bandwidth of measurement data collected for quadcopters is even less than one octave. As the first task, a CNN-based electromagnetic target classifier will be designed for the spherical targets using that spectrally rich simulated database. Then, its performance will be compared to the performance of another classifier that has been already reported in automatic target recognition (ATR) literature as designed by the “Wigner Distribution-Principle Component (WD-PCA) based Feature Extraction” technique using the same target library and the same database. After verifying the effectiveness of the CNN-based classifier design aproach through this comparative investigation, a second CNN-based classifier will be designed for the quadcopter type UAVs using their challenging scattered database of very modest spectral bandwidth. Design details and performances of each classifier will be presented through the thesis discussing the advantages and disadvantages of the CNN-based classifier design approach.


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Citation Formats
R. Güneri, “Automatic target recognition of quadcopter type drones from moderately-wideband electromagnetic data using convolutional neural networks,” M.S. - Master of Science, Middle East Technical University, 2022.