1. Radar Detection Technology
Radar detection is an important method for countering drones. It works by emitting and receiving electromagnetic waves. The radar emits high-frequency waves into the air, and when these waves encounter a drone, part of the waves are reflected back and captured by the radar’s receiving antenna. By measuring the time difference between the transmitted and reflected waves, the radar can accurately calculate the distance to the drone. Additionally, using the Doppler effect, when the drone moves relative to the radar, the frequency of the reflected waves changes. This change, known as Doppler shift, allows the radar to determine the drone’s speed. By adjusting the antenna, the radar can also pinpoint the drone’s azimuth and elevation, offering precise drone location.
Different radar frequency bands have their own characteristics in drone detection technology. Millimeter-wave radar, for instance, has a high resolution due to its short wavelength (typically between 1 and 10 mm) and can clearly distinguish small drones. For example, the YLC-12 radar from CETC in China operates at 34GHz, offering a detection range of 20 kilometers for a 0.1㎡RCS target. However, millimeter-wave radar is limited by weather conditions like rain, fog, or dust, which attenuate electromagnetic wave propagation, reducing detection performance.
Quantum radar, based on quantum mechanics, has extremely high sensitivity and resistance to interference. It can detect drones even in adverse weather, with a range three times that of traditional radar. Quantum radar can break through traditional radar limits and is particularly advantageous for detecting low RCS drones. However, the technology is still under development and has not yet been widely applied due to the large size and high cost of the equipment.
MIMO radar (Multiple Input, Multiple Output radar) is another advanced drone detection technology. It uses multiple transmitting and receiving antennas to track targets, as seen in the DARPA "Spider Network" system. This radar can track up to 200 targets simultaneously with a false alarm rate under 0.5%. By transmitting orthogonal waveforms from different antennas, MIMO radar enhances target detection and tracking while minimizing ground clutter and multipath interference. It is especially useful in complex electromagnetic environments for detecting low-flying drones. However, the complexity of signal processing in MIMO radar requires significant computational power for real-time data handling.
2. Optoelectronic Detection Technology
Optoelectronic detection technology uses light signals to detect drones. Key methods include multispectral fusion, thermal imaging, and lidar. Multispectral fusion combines visible, infrared, and ultraviolet wavelengths to gather comprehensive drone features. The DroneGuard system from Israel integrates these sensor data with a 99.3% accuracy rate in drone detection. Visible light captures drone shapes and colors, infrared detects heat from motors and engines, and ultraviolet identifies unique optical characteristics. This integration helps reduce misidentification in drone detection technology.
Thermal imaging works by detecting heat radiation. The FLIR T1040 thermal camera can identify drone engines from distances of up to 800 meters, even in low light conditions. However, thermal imaging is limited by distance and may struggle with small temperature differences or in high-temperature environments.
Lidar, or Laser Imaging Detection and Ranging, works by measuring the time it takes for laser beams to reflect off objects. Velodyne’s HDL-64E lidar creates 3D models of drone surroundings with high precision. Lidar is excellent for detecting low-altitude drones but is sensitive to weather conditions like rain or fog, which can weaken laser signals and reduce accuracy. It provides detailed data on drone shapes, aiding in drone identification.
3. RF Detection Technology
RF detection technology is based on detecting RF signals between the drone and its control station. Software-defined radio (SDR) enhances this process by analyzing these signals. The Blighter Surveillance Systems B400 radar can scan frequencies from 20MHz to 6GHz and has a 92% success rate in identifying drone communication protocols. SDR technology quickly scans the frequency bands and analyzes the signal’s characteristics, such as frequency, amplitude, and modulation, allowing for precise identification of the drone.
As drone technology evolves, some drones are adopting encrypted communication and frequency-hopping, making drone detection technology more challenging. Signal fingerprinting technology, on the other hand, creates a unique database of RF characteristics for different drone models. The ROSC-1 system from Russia can identify 98% of DJI drones by analyzing the unique signal waveforms of each drone, much like a fingerprint. This technology helps accurately identify drones by comparing real-time signals with the database, offering valuable insight for countermeasures.
4. Acoustic Detection Technology
Acoustic detection uses the noise drones generate during flight to detect their presence. Microphone array technology utilizes multiple microphones to pinpoint the drone's location with high precision. The SRC SoundCatcher system uses a 16-microphone array to detect drone noise up to 2 kilometers away. It employs beamforming algorithms to determine the drone’s location by analyzing differences in time and intensity between microphones.
However, acoustic detection is sensitive to environmental noise. In urban environments or windy conditions, background noise may mask the drone’s sound, reducing detection effectiveness. Deep learning recognition plays an important role in enhancing acoustic detection. The MIT Lincoln Laboratory’s algorithm can distinguish between 12 different drone rotor sounds with 97.6% accuracy. By collecting noise samples from various drone models and training deep learning models, it can identify drone types based on the noise profile