Optimizing Weapon-Target Assignment in Multi-Threat Drone Scenarios with Automation in Battle Management

Jason Adaska headshot

Jason Adaska

Program Manager, Mimir Software

The proliferation of small unmanned aircraft systems (sUAS) has had a profound impact on defense operations, significantly altering modern battlefield dynamics. As the quantity of sUAS and their use on the battlefield increases, an even greater threat involves multiple sUAS acting in concert and without human input.

The term for massive collections of sUAS is a swarm. With respect to military applications, a swarm consists of large numbers of sUAS with the capability of self-organizing – or at least coordinating to achieve a common objective. Swarming overwhelms targets by using mass and attrition in combination with decentralized maneuvers or combined fires from multiple directions. When a dynamic set of targets is coming from multiple directions, engaging the right threat with the right weapon at the right time is no simple task.

Battle Management Challenges in Countering sUAS Swarms

A multi-threat scenario, combined with the rapid pace of events, the element of surprise, limited battlespace awareness, and the potential for deadly consequences, makes battle management more demanding and complex. Currently, battle management systems are not optimized for counter-sUAS or for neutralizing multi-axis threats in a drone swarm environment. A human-in-the-loop is still needed to verify classification, prioritize which threats are most pressing and ensure safe operation of kinetic effectors.

In a swarm attack, tactical decision-making must not only prioritize which threat to counter, but also account for where each target is headed, how fast they are going, what effectors are available both at the present time and over a future time horizon. The decision space entails how to best allocate both countermeasures and situational awareness resources to ensure primacy, among numerous other variables. Actions taken now affect what actions are available in the future (e.g. a sensor or weapon is being used to handle threat A so it can’t support an engagement for threat B). Making the best decisions requires an understanding of how things could play out in the future. Asking a human operator to do this in a complex, dynamic scenario is akin to asking them to play multi-dimensional chess in real-time.

This is where automation in battle management shines – decreasing complexity through the use of artificial intelligence (AI) and advanced algorithms to give warfighters an advantage over adversaries.

Optimizing Weapon-Target Assignment with Battle Management Automation

A key component in battle management and dynamic control of missions is the assignment of weapons to targets. The weapon-target assignment (WTA) problem consists of finding an optimal assignment of a set of weapons of various types to a set of targets in order to maximize the total expected damage done to the opponent. In this type of multi-threat environment, optimal weapon-target assignment is a highly challenging decision space for tactical warfighters. Identifying engagement options, weighing their relative value, calculating their predictive success can quickly surpass the cognitive abilities of humans, particularly when then these tasks must be performed under extremely short decision timelines. 

Rather than focusing on a target simply because it is the highest threat, automation in battle management enables the system to consider all assets and prioritize actions based on the overall outcome. Where traditional battle management applications may opt to ignore a smaller drone first to immediately engage a more dangerous threat, an optimized battle management system would consider the path of that drone, its future role in the battlefield and its overall effects on the outcome. In a given scenario, it may reason that the smaller drone will soon be out of range of the optimal weapon, and moreover, that it is headed towards a sensor that is critical to supporting further engagements. A system with this level of processing and forecasting capabilities, could then reason that it is advantageous to take down the smaller drone first, and then focus on the larger threat looming just within range.

In sum, major advantages of automation in battle management include:

    • Real-time battle plans. Real-time planning that supports tactical decision timelines using state-of-the-art techniques in combinatorial optimization and artificial intelligence.
    • Adaptive planning. As new information arrives and new threats are detected, battle plans are dynamically updated, and sensor and weapon resources are automatically re-tasked.
    • Advanced weapon-target assignment. Considers the unique capabilities of each asset to synergistically achieve the best global solution, incorporating accurate physical models for sensors, weapon-threat geometry, ballistic missile flight trajectories, and interceptor guidance models.
    • Optimized for Counter-sUAS. Enhanced reasoning against sUAS adversaries helps you understand how to respond to drones that are actively adapting to counter measures.Weapon-target assignment is essential to executing operational missions with the highest success rate and a critical part of battle management. Automation in battle management offers a way ahead to change the nature of warfare as increased levels of information continue to be introduced to warfighters. By automating key elements of battle management, commanders can speed relaying of orders and accelerate combat operations and maneuvers, facilitating optimal weapon-target assignment and fire support orders.

Numerica can help you optimize decision-making and weapon-target assignment against sUAS threats in dynamic, multi-threat environments. Fill out the form below to get started.


Mimir Command-and-Control Software interface shows target track

Mimir™ Software

Mimir integrates heterogeneous networks of sensors and weapons. With advanced, automated battle management capabilities, the software enables the fusion of large amounts of data to develop battlespace knowledge and awareness, supporting human decision-makers by identifying and prioritizing warfare resource and course of action options.