Drone Swarms Got Targeting System Updates In Flight In Recent AUKUS Demo

British Lt. Gen. Rob Magowan, the UK’s deputy chief of the defense staff, said: “This test demonstrates the military utility of AUKUS’ advanced capabilities. The military capabilities of our three countries are developing and sharing critical information to improve commanders’ decision-making.”

More details about the exact parameters and circumstances of the trial are limited, but it will involve more than 70 military and civilian defense personnel and industrial contractors, according to the UK Ministry of Defence. The drone fleet includes the Blue Bear Spirit (pictured at the top of this story) and Boeing/Insitu CT220 unmanned aerial vehicles supplied by the UK and Australia.

Private contractors have supplied the Soviet BMP-1 infantry fighting vehicle 105mm FV433 Abbot self-propelled howitzers and OT-90 fighters built in the former Czechoslovakia.

“The trial achieved a number of world firsts, including live training on top of flight models and the exchange and use of AI models on unmanned aerial vehicles by AUKUS governments,” the Australian military statement added.

One of the inherent advantages of a fully networked drone swarm with a high degree of autonomy is the ability to quickly perform various tasks over a wide area. Aided by machine learning and artificial intelligence (AI) technologies, that swarm’s ability to autonomously integrate and begin processing the sensor data it otherwise collects will take it all to another level.

A swarm that moves this way quickly monitors a designated zone on the battlefield, spotting threats, categorizing and geospatiating potential threats, and then relaying all that information to other nodes, from nearby friendly units to rear command centers. Strategic elements in the field can use that information to plan attacks, avoid threats, or otherwise provide situational awareness. Specialists in the rear echelons can combine that information with other information to get a better handle on enemy positions and operations and help predict what their next move might be.

Information may be exchanged between drones and automated target detection systems on other platforms on the ground vehicle involved in this experiment. The US Army, among others, is actively exploring adding automatic target detection capabilities to M1 Abrams tanks and Bradley fighting vehicles. Such network capabilities allow various assets, mounted and unmounted, to be exposed to certain threats even when they are out of sight.

Machine learning and AI have the potential to help sift through that information from the start, reducing the actual human workload, and ultimately providing suggested, immediate courses of action based on that information. Palantir, a US data analytics firm with a long history with US intelligence agencies and the US military, recently unveiled exactly these kinds of capabilities to the public as part of its Artificial Intelligence Platform (software suite). AIP). AIP also includes an AI-driven chatbot that operators can directly engage with for advice.

The mention of “live coaching” targeting “models” during the UK’s testing period in April is also particularly interesting in this context. Automatic target detection systems are only as good as their databases. Therefore, they may have difficulty dealing with the contingencies of previously unknown threats or known ones that may be exposed to sensors in an unusual way due to various types of interference or other factors.

Being able to reprogram algorithms to accurately detect those targets and upload them to drones mid-mission has the potential to be even more game-changing for automated target detection capabilities. The US military is exploring such capabilities, particularly in relation to electronic warfare. In that realm, this is known as cognitive electronic warfare, you can read more about it here.

An absolute “holy Quran” of cognitive electronic warfare, electronic warfare suites feature the ability to automatically update themselves in real-time when unexpected information occurs. That level of capability, aided by machine learning and AI technologies, applies similarly to automated target detection.

April’s challenge in the UK is likely to have a range of wider implications for further research and development and procurement of these types of skills, as well as how they are employed as a result of AUKUS. The event clearly shows that the parties to that agreement will actively share technology in this regard, which will help reduce development programs and cost sharing. Jointly developed capabilities allow US, British and Australian units in the field to easily tap into each other’s data streams and even control drones and other assets back and forth between them.

UK Lt. Gen. Rob Magowan said that accelerating technological advances will deliver the operational advantages necessary to defeat current and future threats on the battlefield. By introducing this, we are determined to cooperate with partners to achieve this. Responsible Development and Deployment of AI.”

“This demonstration of capability is truly a joint effort and a critical step in our joint initiative to stay ahead of emerging threats,” said Abe Denmark, senior adviser to the US Secretary of Defense. “By pooling our expertise and resources through the AUKUS partnership, we can ensure our military is equipped with the most modern and most effective tools to defend our nation and protect the principles of freedom and democracy around the world.”

Overall, the capability test last month at Upavon in Wiltshire appears to have demonstrated a number of significant capability advances, including the ability to update targeting algorithms on drones in flight. Furthermore, through AUKUS, it reflects the ever-expanding cooperation between the United States, the United Kingdom and Australia as close allies on various advanced technologies.