Pilots must be aware of their surroundings and environment to outperform the enemy fleet in air combat. Situation Awareness (SA) is vital. Pilots with a superior SA, compared to that of the enemy are more likely to act correctly and more quickly, which in turn increases their chances of outperforming the enemy fleet. For this reason, SA plays a significant role in the battlefield, and the techniques that provide pilots with SA must evolve with the ever-changing battlefield as air-to-air missiles' effective range increases and their performance improves.

We introduce our work in the SA domain for Beyond Visual Range (BVR) air combat. First, we describe the environment in which BVR air combat unfolds, followed by the research challenges where we address developing machine learning-driven tactics for BVR combat to optimize engagement strategies in complex and uncertain environments. Finally, we present our research results and explain how our approach can be applied to engagements with an arbitrary number of enemies and friendly units while noting that our approach should benefit both manned and unmanned aerial vehicles.

För stridspiloter är det väldigt viktigt att vara medvetna om sin omgivning, vad gäller position och status hos både fiender och egna styrkor. Denna situationsmedvetenhet(eng. Situation Awareness, SA) är avgörande för att piloterna skall kunna agera snabbt och korrekt, och därmed vinna striden.SA är således mycket viktigt, och metoder som förbättrar SA utvecklas därför ständigt, parallellt med övrig utveckling av både materiel och taktik.

I denna avhandling presenteras vårt arbete inom SA för luftstrider där fienden befinner sig på långa avstånd (Beyond Visual Range, BVR). Först beskrivs  forskningsutmaningar med speciellt fokus på maskininlärningsdriven taktik för BVR-strider. Sedan presenterar vi våra forskningsresultat och förklarar hur de kan tillämpas i situationer med både bemannade och obemannade flygfarkoster, och med olika antal enheter på respektive sida.

Beyond Visual Range (BVR) air combat is an essential part of modern aerial warfare, relying on advanced radar, missile systems, and decision-support technologies. This survey provides a comprehensive overview of how simulation and Machine Learning (ML) tools have been used to analyze BVR combat, covering key methodologies, applications, and challenges. We examine how ML enables adaptive tactics to improve behavior recognition and threat assessment to enhance situational awareness. The paper also traces the historical evolution of BVR combat, outlining key engagement phases such as detection, missile launch, and post-engagement assessment. A key focus is on the role of simulation environments in modeling realistic combat scenarios, supporting pilot training, and validating AI-driven decision-making strategies. We analyze state-of-the-art simulation tools, comparing their capabilities and limitations for studying multi-agent coordination and real-time adaptability. This survey's main contributions include descriptions of ML applications in BVR air combat, evaluations of simulation tools, identifications of research gaps, and insights into future research directions. This work provides an overview of how traditional simulation approaches merge with artificial intelligence to enable advanced, effective human and autonomous decision-making systems in dynamic and contested environments.

Tactical decisions in air combat are typically evaluated using experience as a basis. Pilots undergo frequent training in various air combat processes to enhance their combat proficiency and evaluation skills. Having the Situational Awareness (SA) necessary to evaluate the effects of multiple missile threats can often be challenging. This study provides a new method for calculating an aircraft fleet's maneuver flexibility in a Beyond-Visual-Range (BVR) setting. Sustaining a high degree of flexibility is necessary to adapt to unforeseen circumstances in BVR air combat. To do that, we employ Deep Neural Networks (DNN) to capture the result of a highperformance aircraft model in the presence of adversarial BVR missiles. We then modify our approach to calculate the aircraft's maneuverability concerning an opposing fleet, looking at the advantages and disadvantages of several flight formations. Finally, we consider the anticipated threat from an incoming opponent formation and optimize the counter-formation. This methodology offers a more sophisticated comprehension of aircraft maneuver flexibility within a BVR framework and aids in developing flexible and efficient decision-making techniques for air combat.

As the effective range of air-to-air missiles increases, it becomes harder for pilots and Unmanned aerial vehicle (UAV) operators to maintain the Situational Awareness (SA) needed to keep their aircraft safe. In this work, we propose a decision support tool to help pilots in Beyond Visual Range (BVR) air combat scenarios assess the risks of different options and make decisions based on those. Building upon earlier research that primarily addressed the threat of a single missile, we extend these ideas to encompass the complex scenario of multiple missile threats. The proposed method uses Deep Neural Networks (DNN) to learn from high-fidelity simulations and provide the pilots with an outcome estimate for a set of different strategies. Our results demonstrate that the proposed system can manage multiple incoming missiles, evaluate a family of options, and recommend the least risky course of action while accounting for all incoming air-to-air threats.

Military aircraft pilots need to adjust to a constantly changing battlefield. A system that aids in understanding challenging combat circumstances and suggests appropriate responses can considerably improve the effectiveness of pilots. In this paper, we provide a Reinforcement Learning (RL) based system that acts as an aid in determining if an afterburner should be turned on to escape an incoming air-to-air missile. An afterburner is a component of a jet engine that increases thrust at the expense of exceptionally high fuel consumption. Thus it provides a short-term advantage, at the cost of a longterm disadvantage, in terms of reduced mission time. Helping to choose when to use the afterburner may significantly lengthen the flight duration, allowing aircraft to support friendly aircraft for longer and suffer fewer friendly fatalities due to this extended ability to provide support. We propose an RL-based risk estimation approach to help determine whether additional thrust is required to escape an incoming missile and study the benefits of thrust-aided evasive maneuvers. The suggested technique gives pilots a risk estimate for the scenario and a recommended course of action. We create an environment in which a pilot must decide whether or not to activate additional thrust to achieve the intended aim at a potentially high fuel consumption cost. Additionally, we investigate various trade-offs of the generated evasive maneuver policies.

Air combat is a high-risk activity where pilots must be aware of the surrounding situation to outperform the opposing team. The chances of beating the opposing team improve when the pilots have superior situation awareness, thus allowing them to act before the opposing team can do counteractions. In a highly dynamic environment, such as air combat, it can be difficult for pilots to track all adversarial units and their capabilities. In this work, we propose a combination of Monte Carlo Tree Search (MCTS) and Convex optimization to help pilots analyze the situation and be aware of any potential risks associated with missile guidance in Beyond Visual Range air combat. Our process uses MCTS to assess the best action from an opposing aircraft perspective. At the same time, the convex optimization problem searches available aircraft trajectories that enable missile guidance in relation to the opponent's potential actions. The proposed system is intended to support human decisions made by a pilot inside the aircraft or by a remote pilot operating an unmanned aerial system (UAS).

Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.

Air Combat is a high-risk activity carried out by trained professionals operating sophisticated equipment. During this activity, a number of trade-offs have to be made, such as the balance between risk and efficiency. A policy that minimizes risk could have very low efficiency, and one that maximizes efficiency may involve very high risk.

In this study, we use Reinforcement Learning (RL) to create Control Barrier Functions (CBF) that captures the current risk, in terms of worst-case future separation between the aircraft and an enemy missile.

CBFs are usually designed manually as closed-form expressions, but for a complex system such as a guided missile, this is not possible. Instead, we solve an RL problem using high fidelity simulation models to find value functions with CBF properties, that can then be used to guarantee safety in real air combat situations. We also provide a theoretical analysis of what family of RL problems result in value functions that can be used as CBFs in this way.

The proposed approach allows the pilot in an air combat scenario to set the exposure level deemed acceptable and continuously monitor the risk related to his/her own safety. Given input regarding acceptable risk, the system limits the choices of the pilot to those that guarantee future satisfaction of the provided bound.

In this paper, we show how classical tactical forma- tion patterns and flanking strategies, such as the line formation and the enveloping maneuver, can be seen as the result of maximizing a natural formation utility.

The problem of automatic formation keeping is extremely well studied within the areas of control and robotics, but the reasons for choosing a particular formation shape and position is much less so.

By analyzing a situation with two adversarial teams of agents facing each other, we show that natural assumptions regarding the target selection of the agents and decreasing weapon efficiency over distance, can be used to optimize a measure of utility over agent positions. This optimization in turn results in formations and positions that are very similar to the ones being used in practice. We present both analytical results for simple examples as well as numerical results for more complex situations.

Creating new air combat tactics and discovering novel maneuvers can require numerous hours of expert pilots' time. Additionally, for each different combat scenario, the same strategies may not work since small changes in equipment performance may drastically change the air combat outcome. For this reason, we created a reinforcement learning environment to help investigate potential air combat tactics in the field of beyond-visual-range (BVR) air combat: the BVR Gym. This type of air combat is important since long-range missiles are often the first weapon to be used in aerial combat. Some existing environments provide high-fidelity simulations but are either not open source or are not adapted to the BVR air combat domain. Other environments are open source but use less accurate simulation models. Our work provides a high-fidelity environment based on the open-source flight dynamics simulator JSBSim and is adapted to the BVR air combat domain. This article describes the building blocks of the environment and some use cases.