Analysis and decoding of radio signals for remote control of drones

Haluza, Michal; Cechak, Jaroslav

doi:10.1109/ntsp.2016.7747781

Cited by 17 publications

(9 citation statements)

References 1 publication

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“…It also has different modes of usage providing 7, 8 or 14 transmit control channels. It's successor FASSTEST also employs duplex communication [92] .…”

Section: Uav Operationmentioning

confidence: 99%

A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

et al. 2021

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Unmanned Aerial Vehicles (UAV) have revolution- ized the aircraft industry in this decade. UAVs are now capable of carrying out remote sensing, remote monitoring, courier delivery, and a lot more. A lot of research is happening on making UAVs more robust using energy harvesting techniques to have a better battery lifetime, network performance and to secure against attackers. UAV networks are many times used for unmanned missions. There have been many attacks on civilian, military, and industrial targets that were carried out using remotely controlled or automated UAVs. This continued misuse has led to research in preventing unauthorized UAVs from causing damage to life and property. In this paper, we present a literature review of UAVs, UAV attacks, and their prevention using anti-UAV techniques. We first discuss the different types of UAVs, the regulatory laws for UAV activities, their use cases, recreational, and military UAV incidents. After understanding their operation, various techniques for monitoring and preventing UAV attacks are described along with case studies.

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“…It also has different modes of usage providing 7, 8 or 14 transmit control channels. It's successor FASSTEST also employs duplex communication [92] .…”

Section: Uav Operationmentioning

confidence: 99%

A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

et al. 2021

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show abstract

“…Another technique widely used for drone detection exploits the radio frequency (RF) communication signals that are established between drone and remote control. [14][15][16] Ezuma et al have studied the detection and classification of UAVs using RF fingerprints of signals transmitted from the controller to the micro-UAV. The acquired raw signals are divided into frames and transformed into the wavelet domain to remove the bias in the signals and reduce the size of the data to be processed.…”

Section: Radio Frequency-based Methodsmentioning

confidence: 99%

Acoustical unmanned aerial vehicle detection in indoor scenarios using logistic regression model

Iannace¹,

Ciaburro²,

Trematerra³

2020

Building Acoustics

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In this study, the data obtained from the acoustic measurements were used to train a model based on logistic regression in order to detect a quadrotor’s vehicle in indoor environment. To simulate a real environment, we made sound recordings in a shopping center. The sounds related to two scenarios were recorded: only anthropic noise and anthropic noise with background music. Later, we reproduced these sounds in an indoor environment of the same size and characteristics as the shopping center. During the simulation test, a drone placed at different distances from the sound level meter was turned on at different speeds to identify their presence in complex acoustic scenarios. Subsequently, these measurements were used to implement a model based on logistic regression for the automatic detection of the unmanned aerial vehicle. Logistic regression is widely used in pattern recognition of the binary dependent variable. This model returns high value of accuracy (0.994), indicating a high number of correct detections. The results obtained in this study suggest the use of this tool for unmanned aerial vehicle detection applications.

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“…This approach can be generalized to train a classifier (e.g., a neural network) for identifying unique RF transmission patterns from popular commercially available UASs. Different RF techniques for remote UAS control are characterized in [88]. In particular, analog FM's RF signature is compared with that of spread spectrum technique, where the latter is shown to have distinguishable RF footprint compared to FM.…”

Section: A Radio Frequency Signals From Uassmentioning

confidence: 99%

Detection, localization, and tracking of unauthorized UAS and Jammers

Güvenç

Özdemir

Yapıcı

et al. 2017

2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)

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Small unmanned aircraft systems (UASs) are expected to take major roles in future smart cities, for example, by delivering goods and merchandise, potentially serving as mobile hot spots for broadband wireless access, and maintaining surveillance and security. Although they can be used for the betterment of the society, they can also be used by malicious entities to conduct physical and cyber attacks to infrastructure, private/public property, and people. Even for legitimate use-cases of small UASs, air traffic management (ATM) for UASs becomes of critical importance for maintaining safe and collusion-free operation. Therefore, various ways to detect, track, and interdict potentially unauthorized drones carries critical importance for surveillance and ATM applications. In this paper, we will review techniques that rely on ambient radio frequency signals (emitted from UASs), radars, acoustic sensors, and computer vision techniques for detection of malicious UASs. We will present some early experimental and simulation results on radar-based range estimation of UASs, and receding horizon tracking of UASs. Subsequently, we will overview common techniques that are considered for interdiction of UASs.

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Analysis and decoding of radio signals for remote control of drones

Cited by 17 publications

References 1 publication

A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

Acoustical unmanned aerial vehicle detection in indoor scenarios using logistic regression model

Detection, localization, and tracking of unauthorized UAS and Jammers

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