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In recent years, advancements in micro uncrewed aerial vehicles (UAVs) and drones have expanded applications and technical capabilities. With their versatility, mobility, and affordability, drones are utilized across various sectors, from military operations to civilian endeavors like disaster management and delivery services. However, their widespread use has raised security, privacy, and safety concerns. Consequently, there’s a growing focus on developing effective drone detection and classification systems using technologies such as radar, RF analysis, and sensor fusion. Global efforts are underway to establish consistent drone regulations to address emerging challenges in drone governance and security.
Comparison of Satellite, Aircraft and UAV:
Satellite and aircraft-based remote sensing methods offer varying advantages and drawbacks regarding coverage area spectral, spatial, and temporal resolution. Satellites suffer from limitations such as cloud cover hindering data collection, while aircraft face challenges like maintaining steady speeds and hovering. UAVs fill gaps left by traditional methods by providing hovering capabilities, flexible speed control, and higher spatial and temporal resolution. Comparatively, UAVs offer high resolution with moderate availability and operating costs, bridging the limitations of both satellite and aircraft systems.
Significance of UAV Detection:
The increasing use of drones across industries raises concerns about privacy, security, and safety, necessitating effective detection systems. Drones, equipped with various payloads, including explosives and surveillance equipment, pose threats such as drone attacks, illegal smuggling, espionage, and collisions. Recent incidents underscore the urgency for robust counter-drone measures, especially in sensitive areas like airports and borders. With over 150 reported incidents since January 2023, reliable UAV detection systems are needed. However, detecting drones, especially with their evolving technology and ability to infiltrate no-fly zones, presents a complex challenge for businesses and academia.
Key Challenges to UAV Detection:
Detecting and classifying UAVs pose significant challenges due to their diverse sizes, speeds, dynamic behaviors, and similarities to other flying objects like birds or airplanes. The range of UAVs varies from a few meters to several kilometers above the ground, with different altitudes and detection ranges presenting complexities for detection systems. Environmental conditions, such as weather, urban obstructions, terrain, and lighting, further hinder the effectiveness of detection algorithms and sensors, leading to false positives or negatives. Additionally, limited battery life restricts UAV flight durations, necessitating efficient energy consumption and charging strategies to extend operational capabilities.
Addressing these challenges requires ongoing research into novel detection and classification methods alongside advancements in sensor technology. Collaboration among AI, computer vision, and signal processing specialists is essential for developing effective solutions. Overcoming these obstacles ensures the security, privacy, and safety of individuals and critical infrastructure and underscores the interdisciplinary nature of UAV detection research.
Drone Detection Technologies:
Radar-Based Detection:
Utilizes electromagnetic waves to detect and locate objects, offering attributes like distance, velocity, azimuth, and elevation.
Active radar (transmits and receives signals) and passive radar (relies on external signal sources).
There are several common types: surveillance radar (long-range detection), millimeter-wave radar (effective in various weather conditions), Pulse-Doppler radar (detects frequency shifts), Continuous wave radar (continuous signal transmission), and Frequency-modulated continuous wave radar (uses fluctuating frequency for range and velocity determination).
Radio Frequency (RF)-Based Detection:
Detects drones by capturing RF signals emitted by onboard electronics.
It utilizes two receivers to capture signals from drones and controllers.
It often detects drones in no-fly zones by intercepting communication signals between drones and ground stations, typically in the 2.4 GHz ISM frequency band.
Acoustic-Based Detection:
Relies on distinct acoustic signatures generated by drones, particularly from propeller blades.
It uses specialized audio sensors to capture drone noises, analyzing frequency, amplitude, modulation, and duration for detection.
It can identify drones based on various characteristics, such as size, speed, and altitude, employing machine learning algorithms for classification.
Vision-Based Detection:
It involves capturing visual data from drones using camera sensors and computer-vision-based object detection algorithms.
Processes images or videos to detect drones, utilizing image acquisition and object recognition techniques.
Sensor Fusion and Other Methods:
Integrates multiple modalities, such as audio and visual features, radar and visual imaging, RF and image sensors, etc., to enhance drone detection, tracking, and classification.
It utilizes sensor fusion techniques, such as early and late fusion, to effectively merge data from various sensors, increasing the detection system’s robustness and accuracy.
Conclusion:
The survey underscores the burgeoning field of drone detection and classification, which is crucial in addressing privacy, security, and safety concerns amid the rapid proliferation of UAVs. It outlines various detection methodologies like radar, acoustic, RF, and visual approaches, alongside inherent challenges such as diverse drone characteristics and real-world interferences. Integration of multiple sensor modalities emerges as pivotal for robust detection systems, employing fusion techniques like early and late fusion. Furthermore, with the advent of 5G and IoT, RF-based detection utilizing Wi-Fi fingerprinting gains significance.
Sources:
https://www.mdpi.com/1424-8220/24/1/125
https://www.preprints.org/manuscript/201811.0601/v1/download
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