Defending against Intrusion of Malicious UAVs with Networked UAV Defense Swarms

TL;DR

This paper presents a novel networked UAV defense system using self-organizing swarms to detect, intercept, and escort malicious drones outside designated flight zones, demonstrating resilience and effectiveness through simulations.

Contribution

It introduces a modular, fully localized UAV defense system with an auto-balanced clustering process for self-organizing swarms to intercept malicious UAVs.

Findings

The defense swarm can effectively intercept malicious UAVs in simulations.

The system is resilient to communication failures within the swarm.

Simulation results confirm the feasibility of the proposed approach.

Abstract

Nowadays, companies such as Amazon, Alibaba, and even pizza chains are pushing forward to use drones, also called UAVs (Unmanned Aerial Vehicles), for service provision, such as package and food delivery. As governments intend to use these immense economic benefits that UAVs have to offer, urban planners are moving forward to incorporate so-called UAV flight zones and UAV highways in their smart city designs. However, the high-speed mobility and behavior dynamics of UAVs need to be monitored to detect and, subsequently, to deal with intruders, rogue drones, and UAVs with a malicious intent. This paper proposes a UAV defense system for the purpose of intercepting and escorting a malicious UAV outside the flight zone. The proposed UAV defense system consists of a defense UAV swarm, which is capable to self-organize its defense formation in the event of intruder detection, and chase the malicious UAV as a networked swarm. Modular design principles have been used for our fully localized approach. We developed an innovative auto-balanced clustering process to realize the intercept- and capture-formation. As it turned out, the resulting networked defense UAV swarm is resilient against communication losses. Finally, a prototype UAV simulator has been implemented. Through extensive simulations, we show the feasibility and performance of our approach.