Dstl uses AI to gather global radar data and aid UK military

Machine learning software automatically updates the positions of more than 10,000 radars everyday

Radar images

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.

Featured Resources

Managing security risk and compliance in a challenging landscape

How key technology partners grow with your organisation

Download now

Evaluate your order-to-cash process

15 recommended metrics to benchmark your O2C operations

Download now

AI 360: Hold, fold, or double down?

How AI can benefit your business

Download now

Getting started with Azure Red Hat OpenShift

A developer’s guide to improving application building and deployment capabilities

Download now

Recommended

How to become a machine learning engineer
Careers & training

How to become a machine learning engineer

23 Dec 2020
Data science fails: Building AI you can trust
Whitepaper

Data science fails: Building AI you can trust

2 Dec 2020
MLOps 101: The foundation for your AI strategy
Whitepaper

MLOps 101: The foundation for your AI strategy

2 Dec 2020
Realising the benefits of automated machine learning
Whitepaper

Realising the benefits of automated machine learning

2 Dec 2020

Most Popular

SolarWinds hackers hit Malwarebytes through Microsoft exploit
hacking

SolarWinds hackers hit Malwarebytes through Microsoft exploit

20 Jan 2021
How to recover deleted emails in Gmail
email delivery

How to recover deleted emails in Gmail

6 Jan 2021
What is a 502 bad gateway and how do you fix it?
web hosting

What is a 502 bad gateway and how do you fix it?

12 Jan 2021