“ANTI-DRONES – Innovative Concept to Detect, Recognize and Track“ Killer-Drone

COORDINATOR: Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT) – acting through Radar and Surveillance System (RaSS) national laboratory, Galleria G.B. Gerace, 18, 56124 Pisa, Italy;


PARTNER 1:     North Kazakhstan State University (NKZU), Pushkin str. 86, 150000, Petropavlovsk, Kazakhstan, KZ;


PARTNER 2:     Mother Teresa University (MTU), Skopje, Republic of North Macedonia;

In the last decade, the development and the diffusion of aerial drones (UAV – Unmanned Aerial Vehicles) has reached relevant purposes, offering many applications in various economic and scientific/research sectors. Today, the market is growing and involves more of 15 Million drones for year.

This strong growth has also caused a major availability of drones for malevolent scopes, considering the possibility to equip the drones with CBRN and explosives payloads.

The increasing level of terrorism alarm make dramatically concrete the issue of the protection against terrorist attack by killer drones, especially in urban environments where the risk to cause damages is very high. A prime example is the assassination attempt on head of state in Venezuela via a drone.

The capability to detect, recognize and localize such threats, as well as to track them in order to set the response (e.g. neutralization), is the precondition necessary for the protection of the site or asset.

Current solution such as GPS spoofing, hijacking and UAV hacking are technologies highly depending of the technology of killer-drone, so they can be not effective.

The anti-drones system, currently deployed in same airports, are based on platforms that integrates detection, recognition and tracking subsystem with neutralization and effector subsystem. The innovation trend is focalized on methodologies and techniques on multi-sensor detection and recognition based on the integration of heterogeneous technologies.

This research project involves the study of architectural and technology solutions to develop an innovative concept of counter-UAV system for applications in urban scenarios and, in general, for the protection of critical infrastructures or critical assets such as industrial plants, public or government buildings, space ground centres, ..). This concept shall demonstrate an high level of flexibility and adaptability in order to response to the continuous increasing of capability of threating and damage of the killer-drones.


OBJ  1
To improve the effectiveness of Law Enforcement Agencies (LEA) in preventing and dealing with drone-based attack in urban context.  

The proposed framework in the project will facilitate LEA both in automatically acquiring, assessing and responding to situations that might relate to terrorist attacks based on mini/micro drones. The low emission radar used to detect and recognize the killer-drones will dramatically reduce the environmental impact (e.g. ECM pollution) in urban environment.

OBJ  2 To identify, research and develop technologies and methods that would best facilitate the countering to terrorist threats and attacks based on killer-drones, including scenario awareness and rapid alerting of relevant authorities and responders.

The project will research the design and development of a novel anti drones system, which will be able to understand and react to terrorist treats/attacks in urban environment. To this end, the ANTI-DRONES system will be designed as a “mini radar network” and a “data fusion framework” able to generate the situational awareness and to enable the fast detection, recognition and tracking of the threats within the max time to activate the neutralization action.
The ANTI-DRONES architecture will also include actuating elements enabling the alerting of relevant authorities in response to the identification of an drone attack.

OBJ  3 Demonstration based on realistic scenarios, as proof of concept. 

ANTI-DRONES will follow a Concept, Development and Experimentation (CD&E) approach, according to an iterative process where each iteration (or small step) requires feedback from the domain experts as input for making small adjustments on the requirements.