Situational Awareness in Complex Situations

DroneShield’s next generation Electronic Warfare (EW) and Signals Intelligence (SIGINT) work was born from Radio Frequency (RF) and computer vision spectrum analysis innovation. This has evolved over the years as through pioneering technology and countermeasure solutions in the C-UAS industry.  The exponential growth, popularity, and use-case of drone warfare means that modern drones are operating in arguably the densest parts of the RF spectrum. As a result, C-UAS RF detection operating in this environment needs to be able to pull a signal “out of the noise”, while still maintaining a low false-alarm rate. Achieving this using traditional techniques, especially in a very cluttered environment, is very difficult – if not impossible. DroneShield has developed a cutting-edge spectrum awareness capability using proprietary Artificial Intelligence (AI). 

DroneShield’s EW and AI capabilities are applied by multi-domain military customers into the modern battlefield in far more general use-cases. Customers have realised that by leveraging AI, many of the tacit assumptions that were foundational to traditional SIGINT are just no longer applicable or relevant.

DroneShield’s capabilities include identifying Signals of Interest (SOI) to enable threat Indications & Warnings (I&W), threat geo-location and the targeting cycle, and obtaining intelligence.  We build, and assist in building, complete solutions to connect and network within an operationally deployed Intelligence, Surveillance, Reconnaissance, and Electronic Warfare (ISREW) network.

Our Electronic Attack capabilities include directed electromagnetic energy to jam, degrade, disrupt UAS. This includes non-kinetic targeting effects to the targeting cycle to deceive, degrade, neutralise or disrupt adversary communications networks and radiating equipment within the Electromagnetic Spectrum.

Data is a key advantage in AI work, and DroneShield has developed advanced data tagging and cleaning techniques, as well as ability to create synthetic data to complement limited available datasets.

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