DIAT-µSAT: micro-Doppler Signature Dataset of Small Unmanned Aerial Vehicle (SUAV)

Due to the smaller size, low cost, and easy operational features, small unmanned aerial vehicles (SUAVs) have become more popular for various defense as well as civil applications. They can also give threat to national security if intentionally operated by any hostile actor(s). Since all the SUAV targets have a high degree of resemblances in their micro-Doppler (m-D) space, their accurate detection/classification can be highly guaranteed by the appropriate deep convolutional neural network (DCNN) architecture. In this work, an indigenously developed continuous wave (CW) (X-band: 10 GHz) radar is used to build a diversified “DIAT-µSAT” dataset comprising 4849 micro-Doppler signature images of SUAV targets: RC plane, three-short-blade rotor, three-long-blade rotor, quadcopter, bionic bird, and mini-helicopter + bionic bird. All the SUAV targets are operated at different speeds/orientations/rates as follows—revolution per minute (RPM): 200–1740 RPM, flapping rates: 2–4 flaps/s, azimuth angles: 0◦–360◦ at the angle resolution of 45◦, and elevation angles: 0◦–90◦ at the angle resolution of 30◦, tilted (with respect to radar’s boresight) target positions, so as to ensure the diversification in our dataset.