Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Area of Science:

• Aerospace Engineering
• Computer Science
• Electrical Engineering

Background:

• Unmanned aerial vehicles (UAVs), or drones, offer benefits in civilian and military sectors but pose risks like airspace invasion and privacy breaches.
• The increasing prevalence of drones necessitates advanced detection and classification systems to mitigate security concerns.

Purpose of the Study:

• To provide a comprehensive review of current drone detection and classification techniques.
• To highlight novel strategies addressing challenges posed by UAVs' dynamic behavior, size, and speed.
• To examine the advantages and limitations of various detection modalities.

Main Methods:

• Categorization of key detection modalities: radar, radio frequency (RF), acoustic, and vision-based approaches.
• Investigation of threats and challenges associated with UAV characteristics.
• Discussion of sensor fusion and alternative detection methods like Wi-Fi, cellular, and IoT networks.

Main Results:

• Identification of distinct advantages and limitations for each primary detection modality.
• Emphasis on the critical role of sensor fusion for enhanced detection accuracy and efficiency.
• Exploration of emerging detection strategies to combat evolving UAV threats.

Conclusions:

• Advanced detection and classification systems are crucial for managing the risks associated with widespread UAV adoption.
• A multi-modal approach, including sensor fusion, is essential for robust and reliable drone detection.
• Continued research into novel detection techniques is vital to address the dynamic challenges presented by UAVs.