Deep Learning for ClassiďŹ cation of Mini-UAVs Using Micro-Doppler Spectrograms in Cognitive Radar
Figure 1. High-level block diagram of a cognitive radar (adapted from Guerci [2]).
during a measurement campaign at the NLR RPAS Test Centre in Marknesse, The Netherlands. The mini-UAVs included two types of fixed wing aircraft (Robbe Air Trainer 140 and Sky Walker X8), an Align T-REX 550 helicopter, a Mikrokopter QuadroXL quadcopter, and a Mikrokopter OktoXL octocopter, see Figure 2. Measurements of the mini-UAVs were conducted with an experimental continuous wave radar that operates at a frequency of 9.5 GHz and samples the radar signal at a rate of 96 kHz. The complex radar signals are converted to micro-Doppler spectrograms using a short time fourier transform (STFT). The Doppler frequency of the mini-
UAV body is removed to center the Doppler spectrum around 0 Hz. Examples of the measured micro-Doppler spectrograms for all 5 mini-UAVs can be seen in Figure 2. The coherent integration time of the spectrograms is 10.7 ms. The modulations of the radar signal due to the moving parts can clearly be observed.
MICRO-DOPPLER SIGNAL SIMULATIONS In addition to the measurements, micro-Doppler signals have been simulated using computer models of mini-
Figure 2. Photos (1st row), measured spectrograms (2nd row), computer models (3rd row), and simulated spectrograms (4th row) of five mini-UAVs.
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IEEE A&E SYSTEMS MAGAZINE
NOVEMBER 2019
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