Electromagnetic Interference of Unmanned Aerial Vehicles: A Characteristic Mode Analysis Approach
Abstract
A simple Unmanned Aerial Vehicles (UAV) model is developed to computationally and experimentally quantify its electromagnetic scattering characteristics and its susceptibility to interference. Characteristic Mode Analysis (CMA) is evoked to calculate the fundamental modes of the structure. The properties of these calculated modes will allow the prediction of the response of the UAV to various interfering waveforms. Innovative experimental measurements are performed to directly measure the coupled current to the UAV and validate the CMA predictions. The analysis and measurements presented in this work provide the first direct experimental validation of modal behavior and the novel application of CMA to study interference to UAVs and similar systems.
Recommended Citation
M. Z. Hamdalla et al., "Electromagnetic Interference of Unmanned Aerial Vehicles: A Characteristic Mode Analysis Approach," Proceedings of the 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (2019, Atlanta, GA), pp. 553 - 554, Institute of Electrical and Electronics Engineers (IEEE), Jul 2019.
The definitive version is available at https://doi.org/10.1109/APUSNCURSINRSM.2019.8888398
Meeting Name
2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019 (2019: Jul. 7-12, Atlanta, GA)
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Electromagnetic Compatibility (EMC) Laboratory
Keywords and Phrases
Characteristic Mode Analysis; EM Compatibility and Interference.; Unmanned Aerial Vehicles
International Standard Book Number (ISBN)
978-172810692-2
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jul 2019
Comments
This work is sponsored by ONR grants # N00014-17-1-2932 and # N00014-17-1-3016, and University of Missouri-Kansas City, School of Graduate Studies Research Award.