Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review.

Area of Science:

• Engineering
• Computer Science
• Security Studies

Background:

• Advancements in technology have led to the proliferation of low-cost, small unmanned aerial vehicles (UAVs), commonly known as drones.
• While drones offer numerous applications, their misuse poses significant security threats, including smuggling, terrorism, and espionage.

Purpose of the Study:

• To discuss the challenges in drone identification, encompassing detection, verification, and classification.
• To provide an overview of technologies used in modern surveillance systems for drone identification.
• To review promising approaches for drone identification phases, with a focus on FMCW radar.

Main Methods:

• Literature review of existing approaches for drone identification.
• Overview of spatially-distributed sensor networks in surveillance systems.
• Focus on Frequency Modulated Continuous Wave (FMCW) radar technology for drone detection and classification.

Main Results:

• Identified key challenges in drone detection, verification, and classification.
• Highlighted the importance of integrated sensor networks for comprehensive surveillance.
• Emphasized FMCW radar as a cost-effective and robust technology for long-distance drone monitoring.

Conclusions:

• Effective drone identification requires a multi-phase approach: detection, verification, and classification.
• Networked surveillance systems with advanced sensors are crucial for addressing drone-related threats.
• FMCW radar technology shows significant promise for reliable and affordable drone surveillance.