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DIAT-µSAT: micro-Doppler Signature Dataset of Small Unmanned Aerial Vehicle (SUAV)
- Citation Author(s):
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Harish ChandraKumawat
Defence Institute of Advanced Technology (DIAT), Girinagar, Pune 411025, IndiaMainakChakrabortyDefence Institute of Advanced Technology (DIAT), Girinagar, Pune 411025, IndiaA. ArockiaBazil RajDefence Institute of Advanced Technology (DIAT), Girinagar, Pune 411025, IndiaSunita VikrantDhavaleDefence Institute of Advanced Technology (DIAT), Girinagar, Pune 411025, India - Submitted by:
- Harish Kumawat
- Last updated:
- Thu, 09/22/2022 - 01:32
- DOI:
- 10.21227/1x2q-8v62
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Abstract
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.
In our dataset, the total number of spectrogram images generated using the open-field experiments is 4849, and the class-wise details can be found in our journal articles (1) H. C. Kumawat, M. Chakraborty, A. A. Bazil Raj and S. V. Dhavale, "DIAT-μSAT: Small Aerial Targets’ Micro-Doppler Signatures and Their Classification Using CNN," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 6004005, doi: 10.1109/LGRS.2021.3102039. (2) H. C. Kumawat, M. Chakraborty and A. A. B. Raj, "DIAT-RadSATNet—A Novel Lightweight DCNN Architecture for Micro-Doppler-Based Small Unmanned Aerial Vehicle (SUAV) Targets’ Detection and Classification," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-11, 2022, Art no. 8504011, doi: 10.1109/TIM.2022.3188050.
To download the .mat files, for the free educational access, please send an e-mail request to "brazilraj.a@diat.ac.in" and "sunitadhavale@diat.ac.in" mentioning the subject: “DIAT-μSAT Dataset Educational Access Request” from their institutional e-mail id.
The DIAT-μSAT dataset is completely open to academic research. To use the dataset, please cite the following base/original papers:
(1) H. C. Kumawat, M. Chakraborty, A. A. Bazil Raj and S. V. Dhavale, "DIAT-μSAT: Small Aerial Targets’ Micro-Doppler Signatures and Their Classification Using CNN," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 6004005, doi: 10.1109/LGRS.2021.3102039.
(2) H. C. Kumawat, M. Chakraborty and A. A. B. Raj, "DIAT-RadSATNet—A Novel Lightweight DCNN Architecture for Micro-Doppler-Based Small Unmanned Aerial Vehicle (SUAV) Targets’ Detection and Classification," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-11, 2022, Art no. 8504011, doi: 10.1109/TIM.2022.3188050.
Comments
want to use it for classfication