In an era where unmanned aerial systems (UAS) or drones are becoming increasingly prevalent, the need for effective Counter-Unmanned Aerial Systems (CUAS) is more critical than ever. These systems are designed to detect, track, and, if necessary, neutralize unauthorized or hostile drones.  Drone detection is generally done by RF detection systems, by radars, or to provide the most robust solution, both technologies. While RF detection systems can be highly effective, they are usually limited to detection of drones within their library of known RF signals. Drones that use an RF control scheme that is not in their library, or drones that are passively flying waypoints (i.e., not emitting an RF signal) cannot be detected by RF detection systems. Targets such as those must be detected by radar systems, making radar detection capabilities crucial.

However, one of the most significant challenges in using a radar-based CUAS system is distinguishing between genuine threats and benign objects, commonly referred to by operators as “clutter.” Radar systems that can detect and track small drones are also great at detecting other small objects, such as birds, foliage, or even atmospheric phenomena that can obscure or mimic drone signatures. Effective clutter mitigation is, therefore, essential for ensuring that radar-based CUAS systems can accurately and efficiently protect sensitive areas without false alarms or missed threats.

Understanding the Impact of Clutter on CUAS Operations

Clutter in the context of CUAS operations refers to any non-threat object that might interfere with the user’s ability to realize that there is a drone present. While this can range from small birds and debris to environmental factors such as rain, snow, or even strong winds, the overwhelming majority of clutter targets are birds, which in general, will far outnumber the number of drone targets at any given time.

The presence of such biological (bird) clutter obviously complicates the detection and tracking processes. If the CUAS system cannot effectively filter out this clutter, it will lead to false positives (where non-threat objects are incorrectly identified as threats) or false negatives (where genuine threats are overlooked). Both scenarios can have serious consequences. False positives might lead to unnecessary deployments of countermeasures, wasting resources, and potentially causing harm to non-threatening entities. False negatives, on the other hand, can leave a critical area vulnerable to an actual drone threat. Perhaps the biggest impact is that the presence of large quantities of incorrect targets will cause operators to be de-sensitized to real threats, to the point that the CUAS system becomes unusable.

Clutter Mitigation Techniques

Several techniques are employed in clutter mitigation to enhance the effectiveness of CUAS. These include:

Advanced Filtering: Sophisticated filtering techniques can be used to isolate drone signals from clutter. This might involve RCS (Radar Cross-Section) analysis, Doppler shift, or other signal processing methods.

Sensor Fusion: By combining data from multiple sensors (e.g., radar, electro-optical, infrared), a CUAS system can create a more comprehensive picture of the environment, making it easier to distinguish between threats and clutter. This is often combined with Neural-Network (deep learning) techniques to help automate this process (discussed next).

Neural-Network AI Algorithms: These algorithms must be trained on vast datasets to recognize patterns associated with both drones and clutter. In theory, they can improve their accuracy over time in identifying genuine threats while ignoring non-threatening objects. In practice, though, this requires operators to manually classify the threats, then ‘inform the system’ of its previously incorrect assessment, which then improves future classifications. This must be done a massive amount of times, to provide significant improvement. This underlines the biggest problem with neural-network (deep-learning) solutions: they require impractically large training data sets. To be effective, the systems need to be exposed to nearly all combinations of birds (including numbers, sizes, and orientation to the radar) flying at all altitudes, in all directions, along with the same combinations of drone targets (all sizes, orientations, numbers, altitudes and directions). The problem further grows in scope as the range of the radar increases, as it can scan greater areas, increasing the size of the data set.

Heuristic AI Algorithms: Rather than the deep-learning, unguided approach of neural networks, a much more practical AI system is called heuristics; this is a human guided AI approach which requires no training set of data for the system. In this approach, programmers directly code into the software the characteristics of both the clutter and the drones, teaching the system what to look for. This approach requires no inputs from the operator and has been shown to be more practical and robust than the neural network (deep learning) approach to the CUAS problem.

Conclusion

Clutter mitigation is not just a technical challenge but a critical component in the effective operation of CUAS. As the use of drones continues to expand, the ability to accurately detect and respond to threats while minimizing the impact of clutter will be key to maintaining security and safety in a wide range of environments. By properly using advanced clutter mitigation technologies, radar-based CUAS systems can ensure that they remain reliable, efficient, and effective in the face of evolving threats.