The accidental shoot-down of friendly drones is concerning trend in modern warfare. In June 2024 a seminar by U.S. Marine Corps revealed that no fewer of 40% of drones shot down by the Israel Defence Forces (IDF) are in fact their own.
“Friendly fire” has always been an issue in war. It isn't necessarily due to incompetence – in the case of drones, it’s due to drones being tiny, novel threats appearing out of nowhere. Defence radar systems are still struggling to adapt.
In an incoming swarm of drones, its both difficult and crucial to quickly ID each target and to distinguish friend from foe. Enter General Noise Ltd - our innovative radar intellectual property might just hold the key to solving this complex puzzle.
The Drone Identification Crisis
A military unit may have only seconds to ID a drone as friend or foe. This often leads to a "shoot first, ask questions later" approach, resulting in the jaw-dropping rate of friendly fire incidents in Israel and elsewhere. Every military now faces similar challenges as they integrate smaller UASs into their operations. The problem is exacerbated by the fact that smaller drones often lack sophisticated IFF (Identification Friend or Foe) systems found in larger aircraft.
General Noise's Radar Innovation
GN's groundbreaking intellectual property leverages the unique acoustic signatures produced by different objects on the battlefield. These sonic fingerprints can be analysed by reprocessing a standard radar signal. This novel method quantifies noise levels and identifies vehicles via their spectral signature, ideally suited for machine learning systems to rapidly ID the target. The technology has proven effective in lab trials using both 24/60 GHz radars and a standard U.S. police K-band radar speed gun.
Potential Use Cases
Spectral analysis and decibel measurement may be useful in various scenarios, including:
Individual Drone Identification: Can differentiate between specific drones within a swarm, a crucial ability for countering complex drone-based attacks.
Load Configuration Assessment: Helps to determine if a vehicle or vessel is laden or empty, providing valuable intelligence on enemy logistics and threat status.
Battle Damage Evaluation: Can assess the status of targets post-engagement, offering real-time feedback.
Technical Specifications
Computational Efficiency: The system operates with minimal computational resources, capable of running on hardware as modest as a Raspberry Pi.
High Accuracy: Achieves a measurement accuracy of 2 dBA or better, providing precise identification capabilities.
Rapid Processing: Requires only a few milliseconds for data gathering and processing, enabling near real-time target identification.
Development Stage: Currently at Technology Readiness Level (TRL) 5, indicating that the technology is ready for prototype integration into real-world systems.
Compatibility and Integration
The technology is expected to be compatible with various radar types, including:
Frequency-Modulated Continuous Wave (FMCW) radar
Synthetic Aperture Radar (SAR)
Multi-target imaging systems
This versatility ensures that GN's innovation can be integrated into a wide range of existing military radar systems, enhancing their capabilities without requiring complete overhauls.
Range and Environmental Factors
GN’s technology can operate at horizon distance.
Development and Integration
GN anticipates that a 6- to 9-month development program, requiring an investment of a few hundred thousand dollars, could create demonstrator capabilities for various scenarios. This relatively short timeline and modest budget make the technology an attractive option for military organizations seeking to address drone identification challenges quickly.
Professor Robert G. W. Brown, co-founder and CTO of General Noise Ltd
Robert brings a wealth of military R&D experience to the company. His expertise spans crucial defence areas including missile guidance, submarine detection, and remote surveillance. With a history of developing technologies for high-stakes military applications for US and UK governments, Professor Brown's experience makes a central figure in radar-based acoustic detection.
Conclusion
General Noise's radar-based acoustic signature detection system offers a promising solution to the pressing issue of drone identification in modern warfare. As militaries worldwide grapple with increased drone usage, this technology could significantly enhance battlefield awareness and target discrimination capabilities.
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