A review of algorithms, methods, and techniques for detecting UAVs and UAS using audio, radiofrequency, and video applications

Keywords: Drone detection, Deep Learning detection, Machine Learning classification, sound sensors, video sensors, radiofrequency sensors


Unmanned Aerial Vehicles (UAVs), also known as drones, have had an exponential evolution in recent times due in large part to the development of technologies that enhance the development of these devices. This has resulted in increasingly affordable and better-equipped artifacts, which implies their application in new fields such as agriculture, transport, monitoring, and aerial photography. However, drones have also been used in terrorist acts, privacy violations, and espionage, in addition to involuntary accidents in high-risk zones such as airports. In response to these events, multiple technologies have been introduced to control and monitor the airspace in order to ensure protection in risk areas. This paper is a review of the state of the art of the techniques, methods, and algorithms used in video, radiofrequency, and audio-based applications to detect UAVs and Unmanned Aircraft Systems (UAS). This study can serve as a starting point to develop future drone detection systems with the most convenient technologies that meet certain requirements of optimal scalability, portability, reliability, and availability.

Author Biographies

Jimmy Flórez, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa Fuerza Aérea Colombiana, Colombia

PhD. in Engineer, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa Fuerza Aérea Colombiana, Comando Aéreo de Combate Número 5, Rionegro- Colombia, jimmy.florez@fac.mil.co

José Ortega, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa Fuerza Aérea Colombiana, Colombia

MSc. in Information and Communication Technologies, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa Fuerza Aérea Colombiana, Comando Aéreo de Combate Número 5, Rionegro - Colombia, jose.ortega@fac.mil.co

Andrés Betancourt, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa, Fuerza Aérea Colombiana, Colombia

Project management specialist, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa, Fuerza Aérea Colombiana, Comando Aéreo de Combate Número 5, Rionegro- Colombia, abetancur.cetad@epfac.co

Andrés García, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa

MSc. in Information and Communication Technologies, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa, Fuerza Aérea Colombiana, Comando Aéreo de Combate Número 5, Rionegro - Colombia, andres.garciares@upb.edu.co

Marlon Bedoya, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa Fuerza Aérea Colombiana, Colombia

Electronic Engineer, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa, Fuerza Aérea Colombiana, Comando Aéreo de Combate Número 5, Rionegro - Colombia, marlon.bedoya@epfac.edu.co

Juan S. Botero*, Instituto Tecnológico Metropolitano, Colombia

PhD. in Electronic Engineer, Grupo Automática, Electrónica y Ciencias Computacionales, Instituto Tecnológico Metropolitano, Medellín- Colombia, juanbotero@itm.edu.co


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How to Cite
Flórez, J., Ortega, J., Betancourt, A., García, A., Bedoya, M., & Botero , J. S. (2020). A review of algorithms, methods, and techniques for detecting UAVs and UAS using audio, radiofrequency, and video applications. TecnoLógicas, 23(48), 269-285. https://doi.org/10.22430/22565337.1408


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