Abstract
This paper investigates the problem of detection and classification of unmanned aerial vehicles (UAVs) in the presence of wireless interference signals using a passive radio frequency (RF) surveillance system. The system uses a multistage detector to distinguish signals transmitted by a UAV controller from the background noise and interference signals. First, RF signals from any source are detected using a Markov models-based naive Bayes decision mechanism. When the receiver operates at a signal-to-noise ratio (SNR) of 10 dB, and the threshold, which defines the states of the models, is set at a level 3.5 times the standard deviation of the preprocessed noise data, a detection accuracy of 99.8% with a false alarm rate of 2.8% is achieved. Second, signals from Wi-Fi and Bluetooth emitters, if present, are detected based on the bandwidth and modulation features of the detected RF signal. Once the input signal is identified as a UAV controller signal, it is classified using machine learning (ML) techniques. Fifteen statistical features extracted from the energy transients of the UAV controller signals are fed to neighborhood component analysis (NCA), and the three most significant features are selected. The performance of the NCA and five different ML classifiers are studied for 15 different types of UAV controllers. A classification accuracy of 98.13% is achieved by k-nearest neighbor classifier at 25 dB SNR. Classification performance is also investigated at different SNR levels and for a set of 17 UAV controllers which includes two pairs from the same UAV controller models.
Highlights
U NMANNED aerial vehicles (UAVs), or drones, are becoming ubiquitous in modern society
2) We introduce the concept of energy transient for the extraction of radio frequency (RF)-based features and show how effective it is for the classification of the unmanned aerial vehicles (UAVs) controller signals
For an signal-to-noise ratio (SNR) of 25 dB, the results show that the k-nearest neighbor and random forest (RandF) machine learning algorithms are the best performing classifiers, achieving accuracy of 98.13% and 97.73%, respectively, when the three most significant RF-based features are used for the classification of 15 UAV controllers
Summary
U NMANNED aerial vehicles (UAVs), or drones, are becoming ubiquitous in modern society. We propose a multistage UAV detection and an ML-based classification system for identifying 17 different UAV controllers in the presence of wireless interference, i.e., Wi-Fi and Bluetooth devices. We capture control signals from 17 UAV controllers and evaluate the ability of the proposed classification system at different SNRs. For an SNR of 25 dB, kNN and RandF achieve accuracy of 95.53% and 95.18%, respectively, when the three most significant RF features are used. RF PHYSICAL LAYER FEATURES-BASED TECHNIQUES Most of the techniques classified within this category rely on the physical layer characteristics of the RF transmission from a UAV to its controller (or vice versa), such as the amplitude envelope or the spectrum of the RF signal These techniques are sometimes referred to as RF fingerprinting techniques because they utilize the unique characteristics of the RF signals for the detection and classification of the UAVs. Experimental investigations show that most of the commercial UAVs have unique RF signatures which is due to the circuitry design and modulation techniques employed. The detected signal is transferred to an ML-based classification system for accurate identification of the UAV controller
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