Mark Funderburk

Works (3)

Updated: April 5th, 2024 15:05

2023 article

Demonstration of Joint SDR/UAV Experiment Development in AERPAW

MILCOM 2023 - 2023 IEEE MILITARY COMMUNICATIONS CONFERENCE.

By: A. Gurses n , M. Funderburk n, J. Kesler n, K. Powell *, T. Rahman *, O. Ozdemir n, M. Mushi n, M. Sichitiu n  ...

Contributors: A. Gürses n , M. Funderburk n, J. Kesler n, K. Powell *, T. Rahman *, O. Özdemir n, M. Mushi n, M. Sichitiu n  ...

TL;DR: This 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. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: March 25, 2024

2022 article

AERPAW Vehicles: Hardware and Software Choices

PROCEEDINGS OF THE 2022 EIGHTH WORKSHOP ON MICRO AERIAL VEHICLE NETWORKS, SYSTEMS, AND APPLICATIONS, DRONET 2022, pp. 37–42.

By: M. Funderburk n, J. Kesler n, K. Sridhar n, M. Sichitiu n , I. Guvenc n , R. Dutta n , T. Zajkowski n, V. Marojevic *

TL;DR: The vehicle aspects of the testbed, including the AERPAW UAVs, UGVs, as well as the hardware and software choices made by the team, aswell as the experience earned in the past few years are detailed. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: September 18, 2023

2021 journal article

Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 58(1), 27–46.

By: M. Ezuma n, C. Anjinappa n, M. Funderburk n & I. Guvenc n 

author keywords: Radar cross-sections; Radar; Radar imaging; Target recognition; Signal to noise ratio; Drones; Propellers; Akaike information criterion (AIC); automatic target recognition (ATR); Bayesian information criterion (BIC); classification; compact-range chamber; detection; radar cross-section (RCS); unmanned aerial vehicle (UAV)
TL;DR: From the model selection analysis, it is observed 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 well and the best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: February 10, 2022

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