In recent years, drone technology has developed rapidly and its application areas have been continuously expanded, from logistics distribution and environmental monitoring to key scenarios such as military reconnaissance and target strikes. At the same time, the widespread use of drones also brings some safety risks. For example, small drones can be used for illegal invasions, intelligence theft, and even attacks on some critical facilities. Traditional countermeasures often find it difficult to achieve better combat results when dealing with these small drones. Drone detection and identification technology based on artificial intelligence (AI) has become an important way to deal with this threat.

  Traditional drone detection and identification technology is mainly achieved through radar, optical sensors and radio monitoring. Among them, the radar emits electromagnetic waves to detect targets, but when facing small drones with low altitude and low speed, it has low sensitivity and is susceptible to terrain interference. Although optical sensors such as infrared cameras can provide visual information, their detection efficiency is greatly reduced in bad weather or night conditions. Radio monitoring locates the drone by identifying the communication signals of the drone, but it will fail when it encounters an encrypted communication link or a silent drone. In addition, when multiple drones operate in concert, the difficulty of detection and identification will be further increased. Traditional methods have obvious shortcomings in processing massive data and responding quickly, and intelligent upgrades are urgently needed.

  Artificial intelligence technology has significantly improved the efficiency of drone detection and identification. Taking the Italian "KARMA" anti-UAV system as an example, its core technologies include multi-source sensor fusion, intelligent identification and classification, real-time decision-making and response, etc.

  Multi-source sensor fusion: The system adopts a radar-free design and works in concert through radio frequency sensors, infrared cameras and artificial intelligence algorithms. The radio frequency sensor is responsible for scanning the communication signals of the drone and extracting key parameters such as frequency bands and signal strength; the infrared camera conducts real-time monitoring and identifying targets; artificial intelligence algorithms integrate data from each sensor to reduce false alarms and missed reports.

  Intelligent identification and classification: The "KARMA" anti-UAV system can analyze and identify different types of drones, such as civilian quadrotor drones and military fixed-wing drones, and can also judge the flight mode of drones, such as hovering, hovering, cluster formation, etc., evaluate the threat level, and initiate response measures.

  Real-time decision-making and response: After a threat is detected, the "KARMA" anti-UAV system will push information to the command and control unit. The operator obtains air information through the human-computer interface and chooses means such as interference or hard killing. In addition, the RF interference module equipped by the system can block the communication link of the drone and make it land or return; if physical destruction is required, the fire control unit can also be linked, but the final decision-making power is in the hands of the operator.

  Tests show that when dealing with multiple complex threat scenarios, detection systems driven by artificial intelligence show certain advantages. For low-altitude drones, they can accurately capture targets in radar blind spots. In the face of cluster attacks, artificial intelligence algorithms can process multi-target data in parallel, predict flight trajectories, and prioritize intercepting high-threat targets.

  Although artificial intelligence technology has effectively improved the detection and recognition capabilities of drones, it faces many challenges in practical applications. For example, drones may use artificial intelligence countermeasures, which will cause misjudgment of the detection system. There may also be problems with algorithm reliability. The accuracy of machine learning models depends on the completeness of the training data. If the training data does not cover new drones or extreme scenarios, artificial intelligence will miss detection. In addition, system integration is difficult, and multi-sensor collaboration needs to solve technical problems such as delay synchronization and data format uniformity, which still require further optimization and improvement.

[Editor in charge: Gao Qiang]