Electromagnetic Spectrum Mission Support

Interference analysis for EMS dependent capabilities throughout the capability life cycle.

Subject matter expert support for capability development and acquisition.

Highly specialised advice and consulting services for securing spectrum access.

Representation within the Australian Radio Study Group 4 (Satellite Services) to help form the Australian position.

Machine learning (ML) methods, particularly those based on artificial intelligence, hold significant potential in enhancing the sensitivity and accuracy of Electromagnetic Spectrum Operation (EMSO) signal identification, especially in scenarios with short-time observations. This paper provides some insights into various machine-learning approaches for EMSO signal classification and modulation recognition, focusing specifically on communication and radar signals supported by use cases within the electromagnetic spectrum domain.

Physics-based modelling and Model-Based Systems Engineering (MBSE) play a pivotal role in revolutionising the visualisation of C4ISREW systems and their interactions during missions. Through physics-based modelling, complex systems can be accurately represented in a virtual environment, allowing for real-time simulations of C4ISREW aspects of mission scenarios. This approach not only facilitates a deeper understanding of mission dynamics but also empowers  capability managers and acquisition agencies to make informed decisions and optimise system performance. With the ability to iterate and refine digital models rapidly, physics-based modelling and MBSE offer unmatched flexibility, cost-effectiveness, and efficiency in ensuring mission success and enhancing commanders' situational awareness.​

Digital twins have emerged as a transformative technology, seamlessly bridging the gap between real-world data and modelling and simulation artifacts in the realm of C4ISREW. By creating virtual representations of physical systems and their interactions, digital twins enable Model-Based Test and Evaluation (MBTE), mission data augmentation, and accurate prediction of C4ISREW system performance in complex mission environments. Real-world data is continuously fed into the digital twin, providing it with up-to-date information and enhancing its accuracy and fidelity. This integration allows for real-time analysis and validation of system behaviour, facilitating informed decision-making and rapid optimizations. Moreover, by simulating the interactions of multiple systems in a mission environment, digital twins offer invaluable insights into systems-of-systems performance, helping in the identification of potential bottlenecks, vulnerabilities, and opportunities for improvement.​