Detection and Localization of unauthorized drones


  • Rogelio Lozano, DR CNRS (1 PhD student in 3rd year)

          Heudiasyc laboratory, UMR CNRS-UTC 7253

          +33 (0)3 44 23 44 95, This email address is being protected from spambots. You need JavaScript enabled to view it.

  • Boris Vidolov, MdC

          Heudiasyc laboratory, UMR CNRS-UTC 7253

          +33 (0)3 44 23 52 49, This email address is being protected from spambots. You need JavaScript enabled to view it.

  • Pedro Castillo, CR1 CNRS (1 PhD student in 1st year)

          Heudiasyc laboratory, UMR CNRS-UTC 7253

          +33 (0)3 44 23 44 93, This email address is being protected from spambots. You need JavaScript enabled to view it.

Context of the thesis: 

The thesis is part of the project activities of the Laboratory of Excellence (LABEX) at the Université de Technologie de Compiègne (UTC) in France on the Control of Technological Systems of Systems (MS2T) (www.utc.fr/labexms2t).

In spite of the current strict regulations for flying drones around populated areas, there exist a significant number of unauthorized flying vehicles in forbidden places. The risks of these undesired UAVs (Unmanned Aerial Vehicles) go from lack of respect of personal privacy to potentially producing very dangerous accidents. For instance last February a commercial airliner almost collided with a drone in the vicinity of Charles de Gaulle Airport. There is clearly an urgent need to detect and neutralize unauthorized drones in strategic areas like nuclear plants sites, government offices, airports, vulnerable industries, etc.

Thesis Description

Detection and Localization of drones

 This research will focus on detection and localization of a drone. The sensors that will be considered include microphones, optical and infrared cameras, LIDARS, etc... Some of the sensors will be located on the ground while other sensors will be carried onboard several quad-rotor. Preliminary work has been carried out detecting the position of a drone using microphones. The objective of this part of the thesis is to use the Kalman filter approach to propose an algorithm, which will adequately combine the data obtained from the fleet of UAVs to estimate the position and velocity of the drone.

Candidate’s profile:

The candidate should have a solid background on control theory (passivity, formation control, Kalman filter), embedded systems, signal and image processing. Some experience in computer programming and dynamical simulation (Matlab/Simulink).


Laboratory Heudiasyc UMR CNRS 7253

Université de Technologie de Compiègne (UTC)

Centre de recherche de Royallieu

BP 20529 Rue Personne de Roberval

60205 Compiègne cedex –France



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