Enhancing Military Digital Twins: Leveraging Dynamic Data-Driven Application Systems for Complex Operational Scenarios

Project begins 2025.

Digital twins play a crucial role in the integration of human and autonomous systems, or “human-machine teaming”, enabling real-time data integration and scenario modeling that inform the actions of both human operators and autonomous systems. By simulating various operational environments and potential outcomes, digital twins help commanders make more informed decisions, ultimately leading to more successful mission outcomes

In modern military operations, the increasing reliance on smart technologies like drones, digital twins, and autonomous systems has outpaced the capabilities of current data integration and decision-making frameworks. Traditional approaches struggle to dynamically process and adapt to large volumes of real-time data, resulting in limitations in situational awareness, resource allocation, and mission adaptability. Furthermore, as digital twins grow more complex with advancements in sensor technology and human-machine collaboration, maintaining their accuracy and responsiveness in highstakes environments, such as Combat Search and Rescue (CSAR) operations, becomes increasingly challenging. Addressing these gaps requires a robust, scalable framework capable of integrating real-time data to enhance operational decision-making, reduce risks, and optimize resource utilization.

To address the limitations of current digital twin platforms in adapting to the rapidly evolving landscape of military operations, this project aims to develop a Dynamic Data-Driven Application System (DDDAS) framework specifically designed to enhance the capabilities of CSAR missions. The proposed framework will focus on the following key objectives:

1. Fidelity Optimization for Real-Time Data Integration: Implement a fidelity optimizer that continuously assesses and refines the integration of live sensor data, ensuring that digital twins maintain a high level of accuracy and responsiveness even as operational conditions.
2. Introducing Human-in-the-Loop Digital Twins: Enhance the existing digital twin’s capabilities by modeling humans’ interactions with the physical and digitals assets through direct sensory and indirect censored data.
3. Optimizing Man-Machine Collaboration: Develop dynamic stochastic optimization models to improve man-machine collaboration within the DDDAS framework, allowing for real-time adjustments in resource allocation and mission strategies based on continuously updated data.

The overarching goal of this research is to create a robust, scalable system that supports autonomous monitoring and control in high-stakes CSAR operations. By leveraging the principles of DDDAS, the proposed framework will not only enhance current digital twin technology but also lay the groundwork for future advancements in predictive modeling and decision support, ultimately leading to more efficient, precise, and cost-effective military operations.

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