Multi-agent anti-drone architecture using an electromagnetic neutralization assisted by an event-triggered expert system and Blockchain-based approach

The rapid proliferation of drones has raised security, safety, and privacy concerns with their anarchic and malevolent deployment, which represent significant public threats to critical infrastructures. Against this growing threat, advanced holistic anti-drone systems are highly required to reinforce airspace safety and security. Prior to neutralizing a target activity, the anti-drone is required to detect and identify the airborne target. Indeed, countering a foe target’s activity properly, namely a drone requires an efficient anti-drone system with a variety of agents, technologies, and approaches. Although existing scientific contributions have made significant progress in the detection and tracking of drones, they appear incomplete when it comes to proposing a holistic anti-drone architecture with sophisticated and novel countermeasures designed to meet the current needs. As part of the proposed architecture, we seek to address this gap by developing a holistic architecture including the main aspects, units, and phases of an anti-drone. The proposed anti-drone architecture is a Multi-Agent System (MAS) combining AI-driven Transformer-DeepSORT tracking, Blockchain-secured communications, and AI techniques to meet the existing needs. Also, we propose a collaborative electromagnetic neutralization to intercept the target and retrieve the drone’s frame without causing potential harm and damage. In addition to providing a holistic approach to anti-drone operations, the proposed architecture considers regulatory and ethical considerations in drone mitigation, such as drone detection, tracking, threat assessment, and neutralization. Our findings demonstrate that this approach not only enhances tracking accuracy and response time but also ensures higher system resilience against evolving drone threats.