UAV detection refers to the process of identifying and tracking unmanned aerial vehicles (UAV) (commonly called UAV). These detection systems are designed to determine the presence, type, location and movement of drones in a specific area, ensuring that they do not pose a threat to security, security or privacy. The detection methods include the use of radar, radio frequency (RF) analysis, acoustic sensors, visual cameras, and advanced sensor fusion technologies that combine data from multiple sources to improve accuracy.

Why do you need a drone for detection?

The rapid development and widespread use of drones has brought huge benefits to various industries, including delivery services, agriculture, and surveillance. However, this proliferation has also led to increased abuse, causing illegal airspace intrusion, privacy violations, and security threats. The ability to monitor and detect drones hovering in specific airspace is becoming increasingly important.

Here are some basic reasons for requiring drone detection:

Prevent illegal activities: detect drones involving smuggling, unauthorized surveillance or transporting contraband to sensitive areas such as prison or border.

Enhanced security: Protect critical infrastructure, public activities, and restricted areas from potential drone threats, including terrorist attacks or espionage.

Ensure safety: Prevent drones from colliding with manned aircraft and mitigate the risk of drones in busy airspace or densely populated areas.

Privacy: Protect individuals and organizations from invasive surveillance or data collection by unauthorized drones.

What are the differences in drone detection technology?

Technology 1 —— Radar-based detection

The radar system emits radio waves and detects echoes reflected from objects in its path. When a radar wave encounters a drone, part of it is reflected back to the radar receiver. By analyzing the time required for the reflected wave to return and its Doppler shift (frequency change due to motion), the radar system can determine the presence, position, and speed of the UAV. It works with a bandwidth of 3 MHz to 300GHz.

There are two types of radar: active radar and passive radar. Active radar transmits signals and then receives reflected signals to detect objects, while passive radar relies on external sources (such as the sun, stars, cellular signals, and FM broadcasts) to detect objects. Active radar is usually referred to as radar and can be single base (same transmitting and receiving antenna) or dual base (different antenna). Active radar emits continuous wave (CW) or pulse, and CW radar includes step frequency continuous wave (SFCW) radar and pulse radar. The pulsed Doppler radar combines the characteristics of both. It uses micro-Doppler frequency shifts generated by moving blades to detect drones, providing effective signals to distinguish between other objects such as drones and birds.

The common radar types are as follows:

Surveillance radar: used for long-range detection, covering a wide range, capable of detecting drones a few kilometers away, usually working in the X band or S band frequency.

Millimeter wave (mmWave) radar: It uses radio waves with wavelengths of 1 to 10 mm to work effectively in a variety of weather conditions and detect small drones at higher resolution.

Pulse Doppler radar: emitting short radio wave pulses to detect frequency changes caused by drone motion, even in the case of background noise or interference.

Continuous wave (CW) radar: It continuously emits radio waves and analyzes frequency changes in reflected signals to detect drones.

Frequency modulated continuous wave (FMCW) radar: emits an electromagnetic signal of the frequency fluctuates with time. Use the frequency difference between the transmitted signal and the reflected signal to determine the range and speed of the object.

Advantages: effective, can operate in various weather conditions, can detect multiple drones at the same time.

Limitations: Small drones may have small radar cross sections, so they are difficult to detect accurately. Radar systems may also be disturbed by objects such as nearby buildings.

Technical 2 —— RF detection

RF detection systems monitor the electromagnetic spectrum to find signals from UAV communication systems, control links, or other electronic devices. The UAV launches radio frequency signals for control, telemetry, and video transmission. The RF sensors analyze these signals to detect the unique features associated with the UAV. This analysis includes the signal frequency, the modulation characteristics, and other recognition features. By triangulation or using a directional antenna, the RF system determines the direction of the signal source and estimates the distance from the signal intensity. RF detection uses RF sensors to passively monitor and monitor 70 MHz to 6GHz frequencies to find the communication link between the drone and the pilot (receiver) to determine the location of the drone and, in some cases, the location of the pilot.

RF-based drone detection is very efficient because drone components such as radio transmitters and GPS receivers emit energy. The detection system includes the UAV, its controller, and two receivers for the capture of different RF signal bands. Drones typically use RF signals in the 2.4GHz ISM band, which the RF scanner passively detects. Supervised machine learning techniques are used to distinguish between labels like "UAV" and "non-UAV" or different UAV models and functions.

Advantages: effective in chaotic environments, it can detect low-flying drones. Can distinguish between other objects, such as drones and birds.

Limitations: Relying on the drone transmitting detectable RF signals that may not be consistent and, in some cases, may be limited compared to radar. The probe range and accuracy may be influenced by environmental factors such as topography and signal interference.

Technical 3 —— for acoustic detection

UAVs produce unique acoustic features available for detection, thanks to their engines, propeller blades, and aerodynamic properties. The sound produced by the propeller blades is particularly useful due to their larger amplitude. Research in this field focuses on the frequency, amplitude, modulation, and duration of the sound emitted by the drone to detect its presence. The detection system uses sensitive audio sensors (such as microphones or microphone arrays) to capture drone noise. These audio signals are then analyzed using methods such as correlation / autocorrelation or machine learning to determine the presence, type, and capability of the UAV. It covers a spectrum of 20 MHz to 20 KHz.

Advantages: Sound detection is not affected by light conditions and is therefore effective in urban environments where visual and RF detection may be challenging.

Limitations: Limited detection range compared to radar or RF. Acoustic-based UAV detection systems face many challenges, such as distinguishing UAV sound from environmental noise and processing distance-based performance changes. Solutions include data augmentation, combining audio-visual data, and integration of multiple acoustic sensor outputs into array processing systems to enhance probe robustness.

Technical 4 —— Optical and Infrared (IR) detection

Optical and infrared sensors detect drones by vision or based on thermal signals. Cameras or infrared sensors capture the images or thermal signals of the drone. Image-processing algorithms analyze these inputs to detect and track the UAV. It covers all the visible and infrared spectrum from 3 MHz to 300GHz.

Advantages: it can be used effectively during the day or at night. The presence of the drone can be visually confirmed.

Limitations: Depending on visibility and weather conditions (e. g. fog, rain). Restricted by the line of sight requirements.

Technology 5 —— Multi-sensor fusion

Integrate data from multiple sensors (e. g., radar, RF, optics) to improve detection accuracy and reliability. The data fusion algorithm combines information from different sensors to create a comprehensive situational awareness map. This approach compensates for the limitations of each sensor and improves the overall detection performance. No single detection technology can do everything and detect all the drones. This can only be achieved when different technologies are combined with each other and integrate data from sensors into a single software platform that can easily detect drones.

Advantages: improve detection reliability, reduce false positives, and provide more powerful tracking ability.

Limitations: Need for complex data fusion algorithms and the integration of multiple sensor technologies.

How can artificial intelligence, machine learning, and deep learning be used for drone detection?

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) play key roles in various UAV detection technologies, improving their accuracy, reliability, and efficiency. In RF detection, the ML algorithm analyzes the patterns in the communication signal to distinguish between UAV and non-UAV sources, and classifies the technologies such as support vector machine (SVM) and random forest. Acoustic-based detection uses the ML model to identify the unique audio features of the UAV, using features such as the Mayer frequency inversion coefficient (MFCC) and classifiers such as balanced random forest (BRF) and multi-layer perceptron (MLP). Vision-based detection mainly relies on DL model (such as convolutional neural network (CNN)) for real-time object recognition, and uses YOLO and Faster R-CNN to accurately detect and identify drones in complex visual environment.

oreover, sensor fusion technology integrates data from multiple modes, including RF, acoustic and visual sensors, benefiting greatly from artificial intelligence and machine learning, enhancing the robustness of the detection system through early and late fusion strategies. These AI-driven methods can not only improve detection accuracy, but also enable adaptive learning and real-time processing, making it an integral part of modern anti-UAV systems.