Scientists at the Defense Science and Technology Laboratory (Dstl) have applied machine learning software to global radar data to aid the UK's military.
There are thousands of radars around the globe and getting accurate, real-time information on these radars is extremely problematic, time-consuming and costly. This has the additional risk of leaving the UK's armed forces operating with limited information on enemy threats.
But, Dstl has developed 'Moonlight', a system that uses machine-learning algorithms to automatically gather vital data from these radars.
"Moonlight data is fused with other sources to provide situational awareness as well as indications and warnings to deployed frontline units," said Jamie Thomas, a warrant officer with the Royal Navy. "This is critical to support the decision-making process and is key to providing success on operations on a daily basis.
Said to be the only system of its kind, it automatically updates the position of more than 10,000 radars every day using machine learning. This is estimated to save 32,000 hours of manual analytical effort each month. The data gathered is used to help improve planning and post-event analysis of the UK's land and air missions.
Dstl has worked with the defence and cyber systems provider 3SDL Ltd to tackle the problem. This machine-learning software has been developed to automate the process, which allows much greater accuracy on the location and identifies radars in near real-time.
"A key part of our role at Dstl is making sure we protect our people and the platforms they work within," said Alasdair Gilchrist, Dstl's programme manager. "This novel software improves the MOD's knowledge of radar threats, making sure we have safer operations for all of our Armed Forces. It significantly enhances operational effectiveness saves the user time and money."
According to the UK government, the technology has caught the attention of the United States, Canada, Australia and New Zealand, who have all expressed an interest in using Moonlight. Dstl is also designing a version for NATO, which will greatly improve the Alliance's ability to identify and locate threat radars.
