DEWC Systems and AIML collaboration to detect RF frequencies for Defence

DEWC Systems and the University of Adelaide’s Australian Institute for Machine Learning (AIML) have formed a collaborative project that will analyse how artificial intelligence (AI) and machine learning (ML) can be used to detect and classify radio frequency (RF) signals in complex environments. This research extends the valuable and innovative research already undertaken by DEWC Systems and AIML for Phase 1 of the Miniaturised Orbital Electronic Sensor System (MOESS) project.

Dr Paul Gardner-Stephen, DEWC Systems’ Chief Technology Officer, will be spearheading the project. “Radio spectrum is very congested, with a wide range of signals and interference sources, which can make it very difficult to identify and correctly classify the signals present. This is why we are turning to AI and ML, to bring the necessary algorithmic power necessary to solve this problem.

“This will enable the creation of applications that work on DEWC’s MOESS and Wombat S3 (Wombat Smart Sensor Suite) platforms to identify unexpected signals from among the forest of wireless communications, to help defence identify and respond to threats as they emerge,” he added.

From the outset, the goal of MOESS was to create a platform upon which a variety of high-value applications can be run. This work towards an automatic signal classification application is a natural outworking of that plan and works to show the power and flexibility of the MOESS platform. The collaborative project will continue to develop sovereign capability in the application of AI and machine learning and provide novel techniques that can support Defence needs.