New Breakthrough in Anti-Drone Radar Technology: Multi-Band Collaboration and Multi-Mode Integration Reshape Low-Altitude Security Landscape

Anti-drone radar technology is entering a critical phase of rapid iteration. Faced with the evolving threats of drone swarms, miniaturization, and increased intelligence, defense systems are transitioning from single-dimensional detection to multi-dimensional, intelligent solutions. Whether in military battlefields or civilian security, anti-drone radars are continuously upgrading toward the goals of “more precise perception, faster response, and more flexible deployment,” driving a comprehensive restructuring of airspace security governance systems.

Multi-Band Collaboration: Spectrum Fusion and Intelligent Algorithms Drive Accurate Identification

The core breakthrough in future anti-drone radar technology lies in the efficient integration of spectrum resources and the deep fusion of artificial intelligence algorithms. Faced with micro-drones with a radar cross-section of only 0.01 square meters, traditional single-band radars struggle to achieve effective detection, making multi-band collaborative detection an industry mainstream.

For example, the Omega360 system, jointly developed by Italy’s Fincantieri Group and Qatar, integrates high-resolution Doppler radar with AI algorithms. It uses the Ku band for high-precision positioning and the S band for long-range detection, significantly enhancing the ability to capture small and micro targets. The system supports adaptive multi-band switching, automatically optimizing operating frequencies based on target characteristics and environmental interference—like equipping the radar with a “smart filter” to maintain stable performance in complex electromagnetic environments.

Micro-Doppler signature recognition technology has become a key breakthrough in distinguishing drones from birds and ground clutter. Denmark’s XENTA-C radar, for instance, captures the unique frequency signals of drone rotors, effectively solving the challenge of identifying hovering drones. Such physics-based identification methods significantly outperform traditional trajectory and speed analysis in anti-interference capabilities.

In the future, with the continuous optimization of machine learning models, radar systems will be able to extract more multi-dimensional features—including drone material, number of propellers, and even payload type—enabling precise judgment of target models and threat levels.

Software-defined radar (SDR) architecture is accelerating technological iteration. The U.S. Marine Corps’ MADIS MK2 system integrates the RADA RPS-42 radar, using a modular design to achieve precise identification of small and micro targets. It supports continuous performance enhancements through software upgrades without hardware replacement. By 2030, mainstream anti-drone radars are expected to widely support “plug-and-play” algorithm updates, enabling rapid responses to new threats and building a closed-loop defense mechanism of “threat identification-algorithm iteration-system upgrade.”

Multi-Mode Integration: From Detection to Countermeasures in an Integrated Defense System

Anti-drone systems are evolving from single detection functions to integrated “detection, identification, control, and countermeasure” capabilities, making multi-mode integration a key trend for future development.

Taking Wuhan Lakeda’s “Owl” anti-drone system as an example, it comprehensively utilizes four technologies—radar detection, spectrum monitoring, navigation spoofing, and electromagnetic interference—to achieve end-to-end drone handling. It is widely used in public security and critical site protection, promoting the synergistic development of low-altitude security and industry applications.

The “Owl” system consists of detection and control units, electromagnetic interference units, support frames, and command and control units. It offers radar and spectrum composite detection within a range of 2–3.5 kilometers, with spoofing and radio interference capabilities within 1 kilometer. Equipped with high-gain directional antennas, the system delivers 25–30 watts per channel, with an interference range of 1–5 kilometers. It can automatically execute preset defense strategies, enabling immediate response upon detection.

Faced with drone swarm attacks, distributed networking technology has become a critical solution. Poland’s APS FIELDctrl ADVANCE radar employs 3D MIMO active phased array technology, capable of simultaneously tracking hundreds of low-altitude targets with superior multi-target processing and anti-saturation capabilities.

In the future, leveraging 5G/6G communication networks, distributed radar nodes will achieve real-time data sharing and collaborative decision-making, building a three-dimensional defense network covering tens of square kilometers. This will enable system-level optimization in target identification, tracking, and countermeasure task allocation, significantly enhancing regional defense efficiency.

Conclusion: Synergistic Evolution of Technology, Scenarios, and Regulations to Build a Sustainable Low-Altitude Security Ecosystem

The development of anti-drone radar is not just a technological race but a systematic project involving multi-disciplinary integration, regulatory construction, and application scenario innovation. From micro-Doppler signature analysis to cross-domain collaborative networking, and from portable individual devices to wide-area integrated systems, every breakthrough is redefining the boundaries of low-altitude security.

In this ongoing game of offense and defense, only by organically combining technological innovation, practical needs, and ethical regulations can we build a truly robust and sustainable low-altitude security ecosystem, laying a solid foundation for the high-quality development of the low-altitude economy.

Further Reading