kumoh national institute of technology
Networked Systems Lab.

Mohtasin Golam, Rubina Akter, Simeon Ajakwe, Vivian Ukamaka Ihekoronye, Jae-Min Lee, and Dong-Seong Kim, "Deep Learning for Anti-Drone: A Comparative Review on Deep-Learning for Anti-Drone Technology", IEEE Journal of Specific Area of Communication. (P)
By : Mohtasin Golam
Date : 2021-12-21
Views : 125

Unmanned aerial vehicles UAV commonly known as drones are designed to provide numerous beneficial services ranging from package delivery to surveillance. For medical services, drones are used to deliver blood units, ambulance services and other medical services. On the other hand, the drones are utilized into Cinema services such as video recording and image capturing especially in complicated regions. However, the prevalent use of drones poses great threats to public security and personal privacy. These threats are range from illegal activities such as smuggling to those that may be dangerous, such as flying over people at an event, to annoying and invasive activities, such as a neighbor spying. For these reasons, antidrone technology has become such a big issue in recent years. Hence, we need to present what kinds of options are available to the organizations want to get a better sense of the threats that are in their airspace and in turn take relevance actions against. Several types of anti-drone systems are designed based on different techniques including detection and tracking unwanted drone and yet handling their threats. Anti-drone systems are accompanied by some challenges such as detection range, efficient classification methods and suitable mitigation techniques based on drone type as well as target nature.