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Post-doctoral position in signal processing

RF fingerprinting based techniques for drones detection and classification

Published 2/7/2020

Keywords: RF fingerprinting, signal processing, drones, classification, identification, detection algorithms, compressed sampling

Duration: 12 months

Starting date: september/october 2020

RF fingerprinting based techniques for drones detection and classification

 

Title: RF fingerprinting based techniques for drones detection and classification

Post-doctoral position in signal processing

Keywords: RF fingerprinting, signal processing, drones, classification, identification, detection algorithms, compressed sampling

Laboratory: Laboratoire des Sciences et Techniques de l'Information, de la Communication et de la Connaissance, Lab-STICC / UMR CNRS 6285

Duration: 12 months

 Starting date: september/october 2020

Applicant profile:

The applicant should have a PhD in signal processing with expertise in:

  • Digital signal processing,
  • Detection techniques,
  • Compressed sampling techniques,
  • Digital programming.

The applicant must be fluent in English.

Job description:

The use of drones has substantially grown over the past few years and finds its applications in various fields such as surveillance, search and rescue missions, infrastructure inspection, package delivery, etc. However, malicious use by some hobbyists or malevolent actors can present a threat to public safety and security, such as the overflight of certain risk areas (e.g., nuclear power plants). To respond to these threats, a market in the fight against drone is emerging and therefore the development of innovative solutions becomes necessary to guarantee the protection of sensitive infrastructure.

Several techniques have been proposed so far for drones detection and classification. Conventional radar-based techniques [1] are widely used to detect and identify drones, but generally fail to detect micro drones. Similarly, camera-based techniques [2] can be used but require good lighting conditions, high quality lens, and camera with ultra-high resolution for detecting drones at long distance. More recently, RF fingerprinting techniques have been proposed [3]. These techniques are based on the characteristics of the RF signal transmitted by the drone's radio controller or by the drone itself. In [3], the authors were interested in the drone body shifts during the navigation and in the body vibrations caused by the rotating propellers in order to characterize the RF transmitted signal and thus be able to detect its presence.

Under the supervision of two Lab-STICC researchers, the applicant mission will be to conduct research on the drones detection based on the RF signal signature. First, the work will consist of making a state-of-the-art on the "RF fingerprinting" algorithms which take into account the signatures provided by the radio

components defaults or by the signal distortion via the transmitter support platform and then analyzing the radio signals transmitted by the drones in order to highlight these radio signatures. In a second step, an intelligent processing of these radio characteristics will be proposed to best qualify the transmitted information. In addition, the applicant will implement an SDR (Software Defined Radio or Radio Software) type acquisition chain for the drones detection and classification based on the radio signatures. In order to test the proposed algorithms, the signal acquisition chain will be implemented on a SDR USRP type platform using the OpenSources GnuRadio suite and the processing or post-processing of signals for detection, classification and the characterization will be developed under the Matlab environment.

 Supervision team:

The applicant will occupy a post-doctoral position within the SI3 team of the Lab-STICC at the University of Western Brittany. The proposed work takes place in the frame of a project with two industrial partners and an academic 

partner, ENSTA Bretagne. The management team consists of two members: Roland Gautier, Senior Lecturer HDR and Roua Youssef, Lecturer. For all or part of the tasks, the applicant will collaborate closely with the researchers and engineers of the team as well as the industrial partners involved in the project.

 Contact :

To apply, please send a detailed CV with a list of publications / communications as well as a reference letter supporting your skills in the subject by email to:

 Roland Gautier : roland.gautier@univ-brest.fr

Roua Youssef :roua.youssef@univ-brest.fr

 

References

[1] M. Schmidt and M. Shear, “A drone, too small for radar to detect, rattles the white house,” 2015. [Online]. Available:https://www.nytimes.com/2015/01/27/us/white-house-drone.html.

[2] M. A. Ma’sum et al., “Simulation of Intelligent Unmanned Aerial Vehicle (UAV) for Military Surveillance,” 2013 Int’l. Conf. Advanced Computer Science and Inf. Systems, Sept. 2013, pp. 161–66.

[3] P. Nguyen, H. Truong, M. Ravindranathan, A. Nguyen, R. Han and T. Vu, “Matthan: Drone Presence Detection by Identifying Physical Signatures in the Drone’s RF Communication”, Proc. 15th Annual Int. Conf. on Mobile Systems, Applications, and Services, MobiSys 2017, pp.211-224.

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