Dynamic Data-Driven Digital Twin Network Construction and Calibration for AAV Swarms

As an advanced framework, the digital twin network (DTN) provides effective management and decision support for autonomous aerial vehicle (AAV) swarms and has become a recent research hotspot. The effectiveness of many DTN applications relies on the assumption that high-fidelity digital twin (DT) models exist and are readily available. However, constructing such high-fidelity DT models of AAV swarms is a challenging task, especially in complex and dynamic environments. Despite its importance, there is a notable lack of research focused on the construction of high-fidelity DT models specifically for AAV swarms. This study proposes a dynamic data-driven approach for constructing and calibrating DT models of AAV swarms to achieve long-term consistency with real-world AAV behaviors. The method leverages parameter identification to estimate key parameters of DT models and data assimilation to refine and calibrate the model. It can provide high-fidelity DT AAV models for artificial intelligence model training and facilitate AAV swarm DTN from concept to real application. Additionally, this article developed a DT simulation platform for AAV swarms, validating the proposed method through software-in-the-loop simulations and physical testing. Results indicate that the optimal subpattern assignment metric decreases by an average of 79.2% after calibration, significantly improving the DT model’s fidelity.