Modern militaries face new opportunities and challenges - small drones (sUAS). These small drones enhance mission capabilities and pose a serious threat if they are owned by the enemy. To enable small drone swarm warfare, a shift to autonomous, artificial intelligence (AI) -driven countermeasures is needed to optimize sensor resource management for intelligent real-time decision-making and situational awareness. Battlefield sensor resource management optimizes communication between systems, as well as connectivity to larger defense networks.
Today, military operations are influenced by many areas of technology, and small unmanned aircraft systems have become game-changing tools, reshaping tactics and strategic approaches. Longer battery life and robust communications networks enable small drones to carry out autonomous missions, greatly improving their range, endurance and agility. However, these technological advancements also provide adversaries with enhanced capabilities to use small drones to gather intelligence, conduct attacks and possibly even spread chemical, biological or radiological materials, thus severely hindering readiness.
Small unmanned aerial vehicle swarms suppress or weaken targets through continuous attacks. Compared with a single or smaller unmanned aerial vehicle group, swarms have significant advantages, including expendability, stronger resilience and adaptability. When one or more small unmanned aerial vehicles are shot down, the remaining unmanned aerial vehicles can share information, keep communication lines open, and theoretically can instantly reassign mission targets and adapt to changing battlefield conditions in real time.
Limitations of traditional anti-miniature drone methods
Historically, anti-small drone systems have relied on manual maneuvers to provide operators with detailed sensor and weapon data, in large quantities. This information glut not only complicates operator interactions, but also slows decision-making, especially in the face of small drone swarm threats.
As autonomous drones coordinate their actions to achieve their goals, defensive forces must maintain their advantage by leveraging more decision support and automation of anti-small drone resources. Sensor management must be closely tied to operational management objectives. At the same time, operational management support tools must account for the complex relationship between sensor control and weapon deployment.
Leveraging artificial intelligence and automation for operational advantage
Optimize sensor resource management
Sensor resource management involves allocating sensors to optimize detection, tracking, and data fusion. Traditional anti-small unmanned aerial vehicle systems usually require personnel to manage sensor task allocation, resulting in slower speeds and longer response times during attacks. On the other hand, automated sensor management enables software to handle specific sensor tasks, while operators focus on high-level guidance and supervision, thereby making the entire process more efficient.
Automated sensor resource management has several advantages. First, it not only supports operators to focus on higher-level planning and tactics in complex scenarios, but also enables rapid response decisions to multiple dynamic threats by intelligently determining sensor dwell times and target revisit intervals. This approach reduces the risk to troop personnel by deploying automated sensors on unmanned platforms. In addition, it also enables automatic target cue capabilities, with optical systems guided based on radar detection data when targets are visible, all without human intervention. Finally, automated sensor resource management supports the low-signature mission concept, scanning threats and activating radar resources only when needed, thus reducing threats to warfighters while maintaining effective counter-drone operations.
With artificial intelligence and coordinated algorithms, automated sensor resource management increases the accuracy and adaptability of defensive operations, ensuring full utilization of sensor assets and intelligence.
Optimize weapon target allocation
In combat management, it is important to complete the strategic decision of which weapon to use against which targets and when. The problem of weapon targeting involves finding the best way to assign different weapon systems to targets in order to maximize the strike against the enemy. If there are multiple threats in a combat scenario, this decision can be a challenge for humans, who must quickly assess how to engage within seconds to minutes.
By automating key parts of battle management, commanders can accelerate operations and maneuvers, decide which targets to hit and when to provide fire support, which is changing the way wars are conducted as information becomes increasingly abundant.
Integrating sensors into combat management
Drone swarms can generate vast amounts of information, leaving operators with insufficient time to make effective decisions. By implementing solutions that coordinate sensor and weapon resource management for unified results, troops can turn vast amounts of data and a plethora of options into actionable intelligence.
When sensor resource management is aligned with combat management goals, integrated capabilities will exceed individual capabilities. Together, they ensure effective engagement and real-time damage assessment in complex multi-threat situations. Moreover, they also meet mission requirements and support overall strategic goals by customizing responses, thereby making full use of limited sensor and weapon resources. This teamwork reduces the burden on operators in a fast-paced environment by automatically executing simpler tasks while still maintaining the flexibility of human control. The use of artificial intelligence and optimization-driven decision support helps combat managers make faster and more informed decisions, adjusting and adapting to the most effective courses of action in real time.
The future of small drones
Due to the exponential growth of small drone swarms and the increasing complexity of multi-threat scenarios, anti-small drone approaches must be fundamentally reassessed. Traditional approaches, which rely on manual processes and siloed information systems, cannot effectively address the challenges of synchronous, autonomous drone formations.
By using artificial intelligence, automation, and advanced algorithms, modern anti-small drone systems can seamlessly integrate sensor management and combat management, distributing sensors and weapons more efficiently to achieve common goals. This integrated approach not only improves situational awareness and engagement capabilities, but also relieves operators of the psychological burden of making quick, informed decisions in a fast-paced environment.
As the battlefield continues to change, the anti-small unmanned aerial vehicle capability driven by artificial intelligence and algorithms is crucial for maintaining tactical advantages and protecting troops. With intelligent automation, the military can effectively deal with the threat posed by drone swarms and ensure maintaining a key advantage on the tactical edge.
