At present, with the vigorous development of the low-altitude economy, the application of unmanned aerial vehicles (UAVs) is showing an explosive growth trend. However, the resulting safety hazards have become increasingly prominent. Incidents such as unauthorized drones intruding into sensitive areas and disrupting civil aviation operations are not uncommon. Such incidents have sounded the alarm for the regulation of unmanned aerial vehicles (UAVs) and also prompted the rapid rise of the market for UAV countermeasures.
According to Caixin Media, as of December 11, 2024, China Government Procurement Network has released 205 announcements related to drone countermeasures, fully demonstrating the strong market demand for drone countermeasures equipment. At present, more than 3,000 enterprises have devoted themselves to the anti-drone field, actively promoting the innovation and development of anti-drone technology. A full industrial chain ecosystem covering R&D, production, sales and service is gradually taking shape.
The integration of AI technology has brought a qualitative leap to the countermeasures for unmanned aerial vehicles (UAVs). AI technology enables anti-drone systems to identify target drones more quickly and accurately in complex environments, and make autonomous decisions based on the characteristics of the target and environmental conditions, taking effective countermeasures.
In practical applications, AI countermeasures against unmanned aerial vehicle (UAV) devices have established a comprehensive "ear and eye" system. Through the analysis of radio frequency and acoustic signals, the device can capture the frequency bands such as 2.4GHz/5.8GHz during the communication between the unmanned aerial vehicle and the remote control, and simultaneously collect the voiceprint characteristics of the propeller noise, achieving dual verification of electromagnetic signals and acoustic characteristics. Single-station direction finding technology can monitor radio signals to calculate the position of unmanned aerial vehicles (UAVs), while multi-station time difference positioning technology can accurately calculate the target position. At the same time, it uses radio fingerprint information to create a black and white list of UAVs to achieve precise control. Acoustic signal analysis identifies the model of unmanned aerial vehicles by matching the voiceprint feature database, but it is greatly affected by environmental noise.
The multi-sensor fusion detection technology has further enhanced the monitoring capability of the equipment. Link radar, visual/thermal imaging sensors with acoustic arrays to build a three-dimensional monitoring network covering a radius of 3 to 16 kilometers. The 3D radar detection system uses electromagnetic wave reflection to detect the position of unmanned aerial vehicles (UAVs), photoelectric recognition and tracking technology to achieve visual tracking and video evidence collection, and acoustic sensors are used for near-field voiceprint recognition of concealed targets. The combination of infrared thermal imaging and visible light equipment can effectively distinguish the rotor features of unmanned aerial vehicles from the flight trajectories of birds even in foggy nights or electromagnetic interference environments.
Deep learning-driven target recognition technology based on AI large models such as DeepSeek can accurately distinguish normal aerial photography from malicious intrusion behaviors by analyzing flight trajectories, signal characteristics and shape data. The system can also independently learn the characteristics of new types of unmanned aerial vehicles, determine the risk level of unregistered "unauthorized flight" equipment based on behavioral patterns, and conduct dynamic threat assessment according to the depth of intrusion, equipment type and airspace sensitivity, generating a three-level hierarchical response. This includes sending legal flight alerts to the operator, blocking the GPS signals of suspicious devices in a targeted manner, and initiating emergency response measures such as laser countermeasures or electromagnetic suppression.
In the interference and interception stage, interference blocking is currently the most commonly used means for AI countermeasures against unmanned aerial vehicle systems both at home and abroad. Signal blocking technology covers the remote control frequency band of unmanned aerial vehicles (UAVs) by emitting high-frequency noise signals, disrupts the control signal and video return transmission channels, and prompts the UAVs to lose control and return or make emergency landings. Or send forged GPS/Beidou satellite signals to induce the unmanned aerial vehicle to have positioning deviations and enter a safe area for emergency landing. Physical interception technology includes launching netbombs or using interception unmanned aerial vehicles for close-range capture, and is suitable for scenarios where target forensics needs to be retained. As well as using laser or microwave weapons to inflict thermal melt damage on the circuits/body of unmanned aerial vehicles, the response speed can reach the millisecond level.
In practical application scenarios, the airport defense system adopts a combination of radar, electro-optical cameras and electromagnetic interference modules, which can achieve an automatic response from detection to interception within 3 seconds, effectively ensuring the safety of the airport airspace. The vehicle-mounted mobile countermeasures device integrates a radio frequency analyzer and a GPS spoofing module, facilitating rapid deployment and being suitable for temporary security tasks. Through the coordinated operation of multi-dimensional perception technology, AI intelligent analysis capabilities and interference interception technology, the countermeasures system can achieve an effective interception rate of over 98% in complex electromagnetic environments, while keeping the false alarm rate below 0.3%.
Although significant progress has been made in drone countermeasures, many challenges are still faced at present. At the technical level, although there are already various detection and countermeasure technologies, continuous improvement and innovation are still needed. For example, in terms of detection technology, different technologies have their own limitations. How to further improve the detection accuracy and reliability in complex environments is an urgent problem to be solved. At the market level, the global AI countermeasure drone field has yet to produce a countermeasure system that is both cost-effective and mature. The high cost of some high-performance equipment has hindered its large-scale promotion and application. However, devices with better portability are difficult to achieve an ideal state in terms of energy usage efficiency and countermeasures. How to strike a balance among factors such as cost, portability, energy usage and countermeasures has become the key to countering the future development of the AI drone market.
Facing the increasingly severe security issues of unmanned aerial vehicles (UAVs), UAV countermeasures play a crucial role in ensuring public safety and maintaining the security of sensitive areas. With the continuous advancement and improvement of technology and the sustained impetus of market demand, unmanned aerial vehicle (UAV) countermeasure equipment is expected to achieve greater breakthroughs in the future, safeguarding safety in low-altitude areas.