Unmanned Combat Aerial Vehicle (UCAV) dogfight, which refers to a fight between two or more UCAVs usually at close quarters, plays a decisive role on the aerial battlefields. With the evolution of artificial intelligence, dogfight progressively transits towards intelligent and autonomous modes. However, the development of autonomous dogfight policy learning is hindered by challenges such as weak exploration capabilities, low learning efficiency, and unrealistic simulated environments. To overcome these challenges, this paper proposes a novel imitative reinforcement learning framework, which efficiently leverages expert data while enabling autonomous exploration. The proposed framework not only enhances learning efficiency through expert imitation, but also ensures adaptability to dynamic environments via autonomous exploration with reinforcement learning. Therefore, the proposed framework can learn a successful dogfight policy of 'pursuit-lock-launch' for UCAVs. To support data-driven learning, we establish a dogfight environment based on the Harfang3D sandbox, where we conduct extensive experiments. The results indicate that the proposed framework excels in multistage dogfight, significantly outperforms state-of-the-art reinforcement learning and imitation learning methods. Thanks to the ability of imitating experts and autonomous exploration, our framework can quickly learn the critical knowledge in complex aerial combat tasks, achieving up to a 100% success rate and demonstrating excellent robustness.

This paper proposes a three-layer unmanned combat aerial vehicle (UCAV) dogfight frame where Deep reinforcement learning (DRL) is responsible for high-level maneuver decision. A four-channel low-level control law is firstly constructed, followed by a library containing eight basic flight maneuvers (BFMs). Double deep Q network (DDQN) is applied for BFM selection in UCAV dogfight, where the opponent strategy during the training process is constructed with DT. Our simulation result shows that, the agent can achieve a win rate of 85.75% against the DT strategy, and positive results when facing various unseen opponents. Based on the proposed frame, interpretability of the DRL-based dogfight is significantly improved. The agent performs yo-yo to adjust its turn rate and gain higher maneuverability. Emergence of "Dive and Chase" behavior also indicates the agent can generate a novel tactic that utilizes the drawback of its opponent.

In aerial combat, dogfighting poses intricate challenges that demand an understanding of both strategic maneuvers and the aerodynamics of agile fighter aircraft. In this paper, we introduce TempFuser, a novel long short-term temporal fusion transformer designed to learn tactical and agile flight maneuvers in aerial dogfights. Our approach employs two distinct LSTM-based input embeddings to encode long-term sparse and short-term dense state representations. By integrating these embeddings through a transformer encoder, our model captures the tactics and agility of fighter jets, enabling it to generate end-to-end flight commands that secure dominant positions and outmaneuver the opponent. After extensive training against various types of opponent aircraft in a high-fidelity flight simulator, our model successfully learns to perform complex fighter maneuvers, consistently outperforming several baseline models. Notably, our model exhibits human-like strategic maneuvers even when facing adversaries with superior specifications, all without relying on explicit prior knowledge. Moreover, it demonstrates robust pursuit performance in challenging supersonic and low-altitude environments. Demo videos are available at https://sites.google.com/view/tempfuser.

Jets can be flown by A.I. and can even take off, land and participate in dogfights. Yes, you read the headline correctly. The United States Defense Department recently confirmed that artificial intelligence successfully flew a jet similar to an F-16 for 17 hours straight. The jet was flown over a series of 12 flights back in December 2022 at the Edwards Air Force Base in Kern County, California. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER The Defense Department used an experimental plane called the Vista X-62A for the flights.

A modified F-16 fighter jet has successfully flown and fought another aircraft while being entirely controlled by artificial intelligence (AI). During test flights, the jet, known as'X-62A' or'VISTA', performed takeoffs, landings and combat manoeuvres without human intervention for a total of over 17 hours. They took place in December 2022 at the Edwards Air Force Base in California, USA, and showed that it is possible to completely hand over the reigns to AI in battle. The algorithms which powered it were developed by the Defense Advanced Research Projects Agency (DARPA) - the research branch of the US Department of Defense. This marks the first time AI has been used on a tactical aircraft as, prior to this milestone, it had only been used in computer simulations of F-16 dogfights.

Pentagon boffins have for the first time used AI algorithms to automatically control a real F-16 fighter jet mid-flight. Well, OK, at least for the first time they can talk about. The aircraft flown in the experiment, dubbed X-62A or VISTA, was modified and equipped with the right hardware components to run the software developed under the Air Combat Evolution (ACE) program run by the US Dept of Defense's science nerve-center DARPA. Launched in 2019, the program's goal is to revamp aircraft combat by enabling F-16s to automatically dogfight. DARPA envisions machine-learning algorithms assisting pilots with flying and perform tactical maneuvers, with our humans focusing on battle commands, strategy, and firing weapons.

Maverick's next wingman could be a drone. In the movies, fighter pilots are depicted as highly trained military aviators with the skills and experience to defeat adversaries in thrilling aerial dogfights. New technologies, though, are set to redefine what it means to be a "Top Gun," as algorithms, data and machines take on a bigger role in the cockpit -- changes hinted at in "Top Gun: Maverick." "A lot of people talk about, you know, the way of the future, possibly taking the pilot out of the aircraft," said 1st Lt. Walker Gall, an F-35 pilot with the U.S. 48th Fighter Wing based at RAF Lakenheath in England. "That's definitely not something that any of us look forward to." "I'd like to keep my job as long as possible, but I mean, it's hard to argue with newer and newer technology," he said.

Maverick's next wingman could be a drone. In the movies, fighter pilots are depicted as highly trained military aviators with the skills and experience to defeat adversaries in thrilling aerial dogfights. New technologies, though, are set to redefine what it means to be a "Top Gun," as algorithms, data and machines take on a bigger role in the cockpit -- changes hinted at in "Top Gun: Maverick." "A lot of people talk about, you know, the way of the future, possibly taking the pilot out of the aircraft," said 1st Lt. Walker Gall, an F-35 pilot with the U.S. 48th Fighter Wing based at RAF Lakenheath in England. "That's definitely not something that any of us look forward to." "I'd like to keep my job as long as possible, but I mean, it's hard to argue with newer and newer technology," he said.

I feel sorry for Tom Cruise. The next Top Gun movie will probably star an Apple chip designer and a ... [ ] team of LISP programmers. In August this year, eight teams gathered for the three-day final of DARPA's AlphaDogfight trials. The teams had developed Artificial Intelligence (AI) pilots to control F-16 fighter aircraft in simulated dogfights. The winner beat the human USAF pilot in five dogfights out of five.

This content can also be viewed on the site it originates from. On a cloudless morning last May, a pilot took off from the Niagara Falls International Airport, heading for restricted military airspace over Lake Ontario. The plane, which bore the insignia of the United States Air Force, was a repurposed Czechoslovak jet, an L-39 Albatros, purchased by a private defense contractor. The bay in front of the cockpit was filled with sensors and computer processors that recorded the aircraft's performance. For two hours, the pilot flew counterclockwise around the lake.