@article{gurses_funderburk_kesler_powell_rahman_ozdemir_mushi_sichitiu_guvenc_dutta_et al._2023, title={Demonstration of Joint SDR/UAV Experiment Development in AERPAW}, ISSN={["2155-7578"]}, url={http://dx.doi.org/10.1109/milcom58377.2023.10356351}, DOI={10.1109/MILCOM58377.2023.10356351}, abstractNote={The Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) is an outdoor testbed providing the experimenters access to programmable radios and programmable vehicles. A key aspect of AERPAW is its experiment development environment. This demo introduces potential users to the main capabilities of AERPAW’s development environment. The demo exercises the main three flexible testbed capabilities, namely the ability of an experimenter to choose a wireless radio setup, a vehicle setup, and to set up traffic. The experiment is then executed live, and the collected data is post-processed and displayed.}, journal={MILCOM 2023 - 2023 IEEE MILITARY COMMUNICATIONS CONFERENCE}, author={Gurses, Anil and Funderburk, Mark and Kesler, John and Powell, Keith and Rahman, Talha F. and Ozdemir, Ozgur and Mushi, Magreth and Sichitiu, Mihail L. and Guvenc, Ismail and Dutta, Rudra and et al.}, year={2023} }
 @article{funderburk_kesler_sridhar_sichitiu_guvenc_dutta_zajkowski_marojevic_2022, title={AERPAW Vehicles: Hardware and Software Choices}, DOI={10.1145/3539493.3539583}, abstractNote={AERPAW (Aerial Experimentation and Research Platform for Advanced Wireless) is an advanced wireless research platform centered around fully programmable radios and fully programmable vehicles. In this paper we detail the vehicle aspects of the testbed, including the AERPAW UAVs, UGVs, as well as the hardware and software choices made by the team, as well as our experience earned in the past few years.}, journal={PROCEEDINGS OF THE 2022 EIGHTH WORKSHOP ON MICRO AERIAL VEHICLE NETWORKS, SYSTEMS, AND APPLICATIONS, DRONET 2022}, author={Funderburk, Mark and Kesler, John and Sridhar, Keshav and Sichitiu, Mihail L. and Guvenc, Ismail and Dutta, Rudra and Zajkowski, Thomas and Marojevic, Vuk}, year={2022}, pages={37–42} }
 @article{ezuma_anjinappa_funderburk_guvenc_2022, title={Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies}, volume={58}, ISSN={["1557-9603"]}, url={https://doi.org/10.1109/TAES.2021.3096875}, DOI={10.1109/TAES.2021.3096875}, abstractNote={This article presents a radar cross-section (RCS)-based statistical recognition system for identifying/classifying unmanned aerial vehicles (UAVs) at microwave frequencies. First, the article presents the results of the vertical (VV) and horizontal (HH) polarization RCS measurement of six commercial UAVs at 15 and 25 GHz in a compact range anechoic chamber. The measurement results show that the average RCS of the UAVs depends on shape, size, material composition of the target UAV as well as the azimuth angle, frequency, and polarization of the illuminating radar. Afterward, radar characterization of the target UAVs is achieved by fitting the RCS measurement data to 11 different statistical models. From the model selection analysis, we observe that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively poorly. The best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. The performance of the UAV statistical recognition system is evaluated at different signal noise ratio (SNR) with the aid of Monte Carlo analysis. At an SNR of 10 dB, the average classification accuracy of 97.60% or better is achievable.}, number={1}, journal={IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS}, author={Ezuma, Martins and Anjinappa, Chethan Kumar and Funderburk, Mark and Guvenc, Ismail}, year={2022}, month={Feb}, pages={27–46} }