Detection and Classification of UAVs Using RF Fingerprints in the Presence of Wi-Fi and Bluetooth Interference is a paper by Martins Ezuma, Faith Erden, Chethan Kumar Anijappa, Ozgur Ozdemir, and Ismail Guvenc.
This research paper delves into the challenge of detecting and classifying unmanned aerial vehicles (UAVs) amidst the presence of wireless interference signals, utilizing a passive radio frequency (RF) surveillance system. The system employs a multistage detector to differentiate signals transmitted by UAV controllers from background noise and interference signals. RF signals from any source are detected using a decision mechanism based on Markov models and naïve Bayes. With a receiver operating at a signal-to-noise ratio (SNR) of 10 dB and a threshold set at 3.5 times the standard deviation of preprocessed noise data, the system achieves a detection accuracy of 99.8% and a false alarm rate of 2.8%. Secondly, signals from Wi-Fi and Bluetooth emitters, if present, are detected based on the bandwidth and modulation characteristics of the identified RF signals. Once an input signal is identified as a UAV controller signal, it undergoes classification using machine learning (ML) techniques. Fifteen statistical features extracted from the energy transients of the UAV controller signals are utilized in neighborhood component analysis (NCA), with the selection of the three most significant features. The performance of NCA and five different ML classifiers is evaluated for 15 distinct types of UAV controllers. At an SNR of 25 dB, the k-nearest neighbor classifier achieves a classification accuracy of 98.13%. The paper also examines classification performance at various SNR levels and for a set of 17 UAV controllers, including two pairs from the same UAV controller models.
Publication Date- November 2019
Detection and Classification of UAVs Using RF Fingerprints in the Presence of Wi-Fi and Bluetooth Interference contains the following major sections:
- Related Work
- Multistage UAV Signal Detection
- Detection of Wi-Fi and Bluetooth Interference
- UAV Classification Using RF Fingerprints
- Experimental Setup and Data Capture
Open Access Paper. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Authors- Martins Ezuma, Faith Erden, Chethan Kumar Anijappa, Ozgur Ozdemir, and Ismail Guvenc
Post Image- Bandwidth analysis of (a) Wi-Fi signal, (b) Bluetooth signal from Motorola e5 cruise, and (c) Spektrum DX5e UAV controller signal (Image Credit-Detection and Classification of UAVs Using RF Fingerprints in the Presence of Wi-Fi and Bluetooth Interference authors)
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