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Enhancing CUAS Effectiveness: The Power of Target Path Prediction

Much of the focus of counter-uncrewed air systems (CUAS) lies in detecting, tracking and classifying objects within monitored airspace. However, when protecting sensitive sites it is vital to go beyond just understanding where potential threats are, but also where those threats might be in the future. 


This is where capabilities like target path prediction come in. By being able to predict where an object is likely to be, resources can be directed more efficiently, and the nature of potential or future threats to operations can be understood in a more holistic manner. 

These benefits can be realised in a number of ways.



Collision avoidance and area protection

One of the most straightforward ways in which path prediction can improve situational awareness is through understanding if and when monitored objects might enter spaces they shouldn’t. This can provide early warning for a range of safety and security concerns, such as: 


  • Objects predicted to enter a restricted area (e.g. a “no-fly zone”) 

  • Objects predicted to leave a permitted area of operations 

  • Objects that are at risk of collision with each other (i.e. whose predicted future paths intersect)


As drones become ubiquitous in airspaces around the world, such incidents are becoming an increasingly common occurrence. An example of this is the collision of a drone with a news helicopter in Los Angeles in 2019, forcing the helicopter to make an unscheduled landing. There are also a growing number of reports of near misses between drones and passenger aircraft at or near airports, such as Stansted Airport in 2021, where a drone came within 2 m of a Boeing 737, or drone activity leading to the temporary suspension of all flights at Dublin Airport in 2023



Improved interception

By being able to predict where an object will be in the near-term (e.g. the next few seconds), assets that require targeting can be cued onto an object more quickly and efficiently. This can include: 

 

  • Training optical pan-tilt-zoom (PTZ) cameras onto objects for further analysis, removing the need for “chasing” the target  

  • Targeting effectors or other countermeasures onto targets for threat mitigation, such as: 

    • RF jamming systems to disrupt the communications link between the drone and its operator 

    • Cyber takeover methods to force the drone to take specific actions (e.g. land or leave) 

    • Kinetic effectors to physically remove the drone from the airspace


Essentially, predictive capabilities enhance the responsiveness and accuracy of sensor systems and threat neutralisation for safer spaces. 



Enhanced decision making

The additional contextual information provided by object path prediction can provide valuable intelligence to operators or automated systems monitoring counter-UAS data feeds.  


For example, if a radar sensor were to pick up a potential UAS entering a monitored airspace, additional sensors – such as cameras – might be required to investigate and provide additional information. By having a good estimate of where the potential threat is headed, the most appropriate sensors can be selected for the job. 


Once a threat is confirmed – and mitigating action required – the same contextual information can be used to deploy the most appropriate effector for the task. 



Support to Uncrewed Traffic Management

Uncrewed Traffic Management (UTM) systems are analogous to air traffic management for conventional aircraft but for UAS – with a focus on enabling the use of autonomous systems in a safe and controlled way. 


The ability to predict the future path of objects can assist in the aim of UTM systems by helping them understand “non-cooperative” aircraft behaviour. Typically, well-behaved or “cooperative” aircraft will provide a UTM system with information about their flight plans and entire flight path so that the system can plan for and manage this traffic appropriately.    


For those objects which cannot or do not provide this information, the additional context that can be provided by a path prediction algorithm can help a UTM system mitigate any potential disruption caused by these non-cooperative objects. This can be done in tandem with a CUAS system’s capabilities to take action against these targets should they threaten the safety or security of an airspace. 



Strengthened CUAS with Path Prediction

Overall, object path prediction capabilities can provide significant benefits to a CUAS system by providing additional context and insights into the behaviour of monitored objects. From enhancing the enforcement of no-fly zones to improving the precision of sensor targeting and the effectiveness of mitigation measures, a path prediction algorithm enhances the capability of a connected system. 


At Dynamic Intelligence Solutions, our Octa system leverages advanced algorithms to predict a target’s future path and classify its type and intent. This enhances the capabilities of connected CUAS sensors and systems in a complementary and holistic manner. By integrating these predictive insights, Octa enables faster and more accurate decision-making, optimising the deployment of countermeasures and ensuring robust protection against aerial threats. Whether safeguarding critical infrastructure or maintaining secure airspace, Octa provides a complementary solution for modern CUAS needs.  

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