The proliferation of uncrewed aerial systems (UAS) has created a significant security challenge for military, government, and commercial organisations. The ability to detect and respond to UAS threats in real-time is critical to preventing disruptions, protecting people and assets, and maintaining operational continuity.
This threat has led to an increase in the development and adoption of counter-UAS (CUAS) systems by those with a requirement to protect sensitive sites from UAS threats. Many of these systems have radar systems as a core component of their sensing and detection capabilities as a powerful and effective means of detecting and tracking objects (including UAS) at scale in a wide range of conditions.
Traditionally, radar systems have had limitations when it comes to detecting small, low-flying, or stealthy objects. However, the application of complementary software techniques can go some way to mitigate these, significantly enhancing CUAS radar capabilities and enabling more effective detection, tracking, classification, and response to these threats.
Improving detection accuracy
Algorithms can analyse radar signals in real-time, enhancing the accuracy of UAS detection and reducing false alarms. By leveraging machine learning and artificial intelligence (AI) techniques, software can identify patterns and anomalies in radar data, improving the detection of small or slow-moving UAS.
Classification at range and at scale
Many CUAS radars have a built-in classification capability, and are able to make estimates of whether tracked objects are UAS or of another type (e.g. birds). This is usually done using a technique called micro-Doppler; this is a method which detects small, repetitive motions of tracked objects such as spinning rotors or flapping wings.
This is an effective technique, but can be prone to misclassification. Additionally, radars with micro-Doppler can often detect objects at a much greater distance than they can classify them; this means that at greater distances from the radar, this classification ability is lost.
Software techniques – such as Octa’s dynamics-based classification – can provide a valuable “second opinion”, providing estimates of the type of tracked object based on different characteristics of the object, leading to more robust and reliable classification. In Octa’s case, because it does not rely on micro-Doppler signals, it is also able to classify targets out to the limit of detection of the radar, improving the effective range of a radar’s classification capability.
Predicting UAS paths: staying ahead of the threat
Beyond target tracking and classification, software can also be used to predict the future path of a tracked UAS. This enables organisations to anticipate the drone's future location, altitude, and velocity, allowing for more effective response and mitigation strategies.
By predicting the path of a detected UAS, organisations can optimise countermeasure deployment, improve resource allocation, and better mitigate the potential impacts of UAS incursion into prohibited areas. Predictive analytics enable proactive measures to be taken, maximising the chances of successful neutralisation while minimising risks to people and assets.
As part of Octa’s target tracking and classification suite, it is capable of providing these short-term path predictions for targets, enabling better CUAS management and response.
Enhancing multi-sensor integration
Next-generation counter-UAS radar systems rely on the integration of multiple sensors, including radar units, cameras (electro-optical and infrared), radio frequency detectors, and acoustic sensors. The complex and differing data feeds from these systems must be effectively combined into a holistic operational picture.
Software - such as command and control (C2) systems – can enable seamless integration of these sensors, helping to build a “single source of truth” by combining their output and the data they generate into a holistic picture. Systems like Octa can also analyse the data from multiple sensors, either together as a “correlated” data stream from a C2 system or as individual data feeds, to provide enhanced situational awareness.
These techniques help to provide a more manageable understanding of the airspace and facilitate more effective tracking and response to UAS threats.
Accelerating response times
The use of advanced software can help to automate many aspects of the detection-to-response cycle, significantly reducing response times and enabling organisations to respond quickly and effectively to UAS threats.
This can be as simple as automatically alerting operators to newly detected potential threats for manual analysis, through to more complex solutions such as the automated targeting of sensors such as cameras to verify potential threats detected by radar systems and – if verified – forwarding this information to first responders for action.
Through this deep integration and intelligent automation, such software can enable the rapid coordination of responses across multiple agencies or teams, and at a level commensurate with the threat detected.
Enhancing situational awareness
A common issue – and one not limited to CUAS – is how best to enable operators to understand and make correct decisions based on a large volume of incoming data, without overwhelming them or causing unnecessary fatigue.
The intelligent processing of this data prior to it reaching the operator is key to this aim; by automating threat detection and the analysis of sensor feeds, the operator can focus on only the high-priority and actionable activity in the airspace, rather than being distracted by unimportant information and other “clutter”.
This is supported by the use of real-time visualisation and analytics tools, to ensure that the data is presented in a clean and understandable way. Together, this enables operators to quickly understand the airspace situation and make informed decisions. This enhanced situational awareness is critical for effective response to UAS threats, whilst minimising the risk of false alarms or unnecessary response actions.
Conclusion
The threat posed by UAS is evolving rapidly, and traditional radar systems are often inadequate to detect and respond to these threats effectively. Advanced software can significantly enhance counter-UAS radar capabilities, improving detection accuracy, enhancing multi-sensor integration, accelerating response times, and providing enhanced situational awareness.
By leveraging next-generation software, organisations can stay ahead of the UAS threat and protect people, assets, and operations from this emerging risk.
Octa can form an important and complementary part of such capabilities. By integrating our dynamics-based tracking, classification and analysis capability, the ability of CUAS detection and monitoring systems is enhanced, allowing them to detect and identify airspace threats earlier and with more confidence – whether it is a single sensor or a complex installation.
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