Non-Cooperative Low-Complexity Detection Approach for FHSS-GFSK Drone Control Signals

Abstract

The commercial drone market has substantially grown over the past few years. While providing numerous advantages in various fields and applications, drones also provide ample opportunities for misuse by irresponsible hobbyists or malevolent actors. The increasing number of safety/security incidents in which drones are involved has motivated researchers to find new and ingenious ways to detect, locate and counter this type of vehicles. In this paper, we propose a new method to detect frequency hopping spread spectrum-Gaussian frequency-shift keying (FHSS-GFSK) drone communication signals, in a non-cooperative scenario, where no prior information about the signals of interest is available. The system is designed to detect and retrieve data bit sequences through a compressive sampling approach, which includes the extraction of the reduced spectral information and a soft detection algorithm. The performance of the proposed approach is assessed in terms of bit error rate and compared with that of a Viterbi detector and a neural network-based detector. The effectiveness of the method described in the paper highlights the fact that current UAV communications are not infallible and present real security issues. INDEX TERMS Counter-drone measures, FHSS-GFSK communication signals, non-cooperative data detection, compressive sampling, security of UAV communications.

DroneSense: The Identification, Segmentation, and Orientation Detection of Drones via Neural Networks

“…To address the problem of drone characterization a wide variety of machine learning assisted drone detection systems have been developed. For example, radio based methods, which eavesdrop on the communications between drones and pilots and apply the statistical analyses of control signals [8]- [11], Convolutional Neural Networks (CNNs) analysing the spectragram [12]- [15], K-Nearest Neighbours (KNNs) [16] clustering of signals, cyclostationary feature extractors [17], decision trees [18] and random forest techniques [19], bitanalysis [20], and, residual [21], recurrent [22] and hierarchical networks [23]. Additionally, acoustic based methods analysing the noise of a drones motors and propellers have also been developed using Mel Frequency Cepstral Coefficients (MFCC) [24]- [29] or by converting the signal to a spectragram [27], [30], [31].…”

Parameter estimation of multiple frequency‐hopping signals based on Bayesian compressive sensing and atomic norm soft thresholding with missing observations

“…Frequency-hopping (FH) signals are generated by varying the carrier frequency according to a pseudo-random pattern. Owing to their inherent advantages, namely a low probability of being intercepted, flexible networking capabilities, resistance to jamming and multipath fading, FH signals have become an appropriate choice and are widely used in satellite communications [1], wireless communications [2,3], physical layer security [4,5], smart grids [6,7], underwater communication [8,9], Internet of Things (IoT) technology [10,11], and unmanned aerial vehicles (UAV) [12,13]. In the fields of interference analysis and communication security, estimating the parameters of FH signals and tracking them is a highly challenging, but crucial, task when the hopping patterns are unknown.…”

New Approach of UAV Movement Detection and Characterization Using Advanced Signal Processing Methods Based on UWB Sensing

“…Based on the bandwidth and modulation feature of the RF detected signal, in [ 20 ] it is used, in the first stage of the drone classification algorithm, a two-state Markov model that decides if the signal comes from a drone controller or an interference source. Other approaches aim to minimize the complexity of the algorithms involved in drone passive detection and propose new specialized methods depending on the physical layer protocol used by the drone [ 21 , 22 ]. Currently, the drone industry provides autonomous guidance capabilities, based on global positioning system (GPS), and makes drones flying in fully autonomous mode, impossible to be detected by RF passive techniques.…”