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Rising importance of AI in Missile Defense as Air & Missile threats become Hypersonic

Militaries around the world are increasingly facing a formidable strategic and threat environment in terms of complexity, lethality, range, sophistication, and number of threats. These range from Fifth-generation stealth fighters, unmanned air vehicles,  more maneuverable and precision-guided ballistic, cruise, and Hypersonic missiles that are becoming widely proliferated to become more accessible to emerging nations.


Hypersonic refers to aircraft, missiles, rockets, and spacecraft that can reach atmospheric speeds in excess of Mach 5, which is almost 4,000 miles per hour or 6,125 kilometers per hour or more.  Flying along the edge of space while gliding and maneuvering these missiles would strike targets with unprecedented speed and precision. They may be loaded with nuclear warhead to target cities and government centers. or non-nuclear packages which can be used to destroy facilities, communications, or weapons and generally cripple capabilities.  These missiles can also adjust direction very rapidly, meaning that it is almost impossible to tell where they will strike. Hypersonic missiles can also adjust mid-air to change target. The high maneuverability and the hypersonic speed make it very difficult to be intercepted by exo-atmospheric kill vehicles as well as lessens the time it can be detected, fired at, or reengaged if there is a miss.


The Pentagon is pursuing new technologies — such as a next-generation interceptor and space sensors — and upgrading existing capabilities to deal with the growing threats. It wants a networked, layered defense architecture that can engage enemy missiles in the boost phase, midcourse and terminal phases of flight.


As faster and more maneuverable threats emerge, the missile defense mission becomes harder. “The unpredictability … is very challenging,” he said. “It challenges your sensor architecture, it challenges your fire control and it challenges the methods by which you engage.  AI could enable the U.S. military to accelerate response times and reduce the probability of human error, Missile Defense Agency Director Vice Adm. Jon Hill noted during remarks at the Center for Strategic and International Studies in Washington, D.C.


Sarah Reeves, vice president of missile defense systems at Lockheed Martin’s space division, said there needs to be a better integration and multi-domain fusion of data. Leveraging machine learning and machine-to-machine interfaces to buy more time for warfighters to make decisions is one way to get after the problem posed by hypersonic weapons, she noted.


Britain’s Royal Navy Testing AI to Counter Missile Attacks for the First Time in 2021

The UK’s Royal Navy is testing the accuracy and effectiveness of artificial intelligence (AI) to defeat missile attacks for the first time at sea. The test of the leading-edge software is being done against supersonic, ballistic, as well as cruise missiles during the largest exercise of its kind off the coasts of Scotland and Norway, the navy said. The trial, that offers a glimpse of the future of air defence at sea, is part of NATO’s Formidable Shield exercise and involves three British warships — destroyer HMS Dragon and two frigates HMS Lancaster and HMS Argyll .


The trial, being led by Defence Science and Technology Laboratory (DSTL) scientists, is testing two AI applications — Startle and Sycoiea, the Royal Navy said in a statement published in its official online news outlet. The Startle application provides real-time recommendations and alerts to sailors monitoring the “air picture” from the operations room. It is designed to help “ease the load” on sailors. And the Sycoiea builds on these alerts to help sailors identify the threat and advise the best weapon to deal with it quickly “than even the most experienced operator.”


During the trial Seaman Sean Brooks aboard HMS Lancaster said he was impressed by the cutting-edge software. “I was able to identify missile threats more quickly than usual and even outwit the operations room,” he said. Experiments with AI have been conducted before, but it’s the first time the system is being tested against live missiles, said Lancaster’s Weapon Engineer Officer Lieutenant Commander Adam Leveridge. “A glimpse into our highly autonomous future.”


The navy is testing these AI-based applications to be able to harmonise their response and look for improvements needed to ensure they work alongside existing radar and other systems. Lancaster’s Commanding Officer Will Blackett said the scale of the naval exercise and the assets and technology involved made it a hugely beneficial experience for all.



Artificial Intelligence Application for Air and Missile Defense Combat Identification, Planning and Weapon Assignment SBIR launched in 2020

Project Office Integrated Air & Missile Defense (IAMD) is the lead materiel developer of the Army’s Integrated Air & Missile Defense (AIAMD) Integrated Battle Command System (IBCS). IBCS will fuse multiple Sensors into a Single Integrated Air Picture for Air & Missile Defense engagement planning and execution. The objective of this SBIR submission is to develop a system architecture and algorithmic framework engine that supports artificial intelligence (AI)-based algorithms used to perform diverse functions such as Defense Design, Identification and Classification of tracks, Predictive Track Vectors, Sensor and Weapon Management, and other potential IAMD functions.


Advances in artificial intelligence (AI) and deep learning techniques, in conjunction with rapid growth in GPU hardware performance have opened up new possibilities for exploitation of AI to perform highly complex tasks with performance exceeding that of more traditional approaches. Potential applications within the Army Integrated Air and Missile Defense (IAMD) system include Identification and Classification of tracked objects, Defense Design, and Dynamic Planning and Tasking.


To support AI / Deep Learning-based applications, the Army requires a robust, scalable architecture, framework and algorithm engine that can be utilized by multiple AI applications in an easily maintainable and extensible manner. The architecture and framework should include a combination of hardware and software that has a straightforward path of integration with current IAMD systems. The AI Engine should support integration and execution of multiple, simultaneous AI applications, as well as the ability to ingest, store, and process significant amounts of data. In addition to execution, the architecture, framework and engine should support training of the algorithms, which minimizes hardware and software costs as well as permits on-the-fly enhancements to the different applications.




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