1,720,968 research outputs found
Data-Driven Methods for Enhanced Situation Awareness in Beyond Visual Range Air Combat
No full textPilots 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.QC 20250306</p
Data-Driven Methods for Enhanced Situation Awareness in Beyond Visual Range Air Combat
No full textPilots 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.QC 20250306</p
A Model Predictive Approach to Satellite Formation Control
No full textIt is notoriously costly to send a large satellite full of on-board instruments to space forplanetary exploration and distributed physical experiments. What if, instead, we could split onelarge instrument into several smaller parts, and then launch each smaller part with a smallersatellite? The problem of having to launch big satellites would be solved; moreover it would alsobe cheaper to manufacture and launch several small satellites that one big satellite.But, unfortunately, there is no free lunch. Splitting a big single satellite into a swarm ofsmall micro satellites introduces the problem of maintaining these micro satellites in a certainposition with respect to each other: indeed if we want our instruments to work together, on-boardinstruments should maintain a certain relative distance and orientation between themselves. Inother words, there is the need to achieve and maintain formations, and continuously counteractthe slight differences in disturbances that will act on each satellite.This thesis then considers this need, and proposes control algorithms that guarantee swarmsof satellites to maintain a certain position in space and counteract spatial disturbances.Once again there is no free lunch: to maintain a formation and be robust with respect todisturbances there is the need for using on-board fuel. But on-board fuel is finite: as soon as itruns out, the satellites won’t be able to change their orbit anymore. Our mission is then to finda control strategy that not only guarantees the satellites to maintain a formation irrespectivelyof the disturbances, but also to minimize fuel usage, so to increase the life of the satellites inorbit.For all these purposes we derive and simulate a tailored stochastic centralized Model Pre-dictive Control (MPC) approach for keeping satellites in formation while considering referencepositions, fuel cost and relative position between satellites in its formulation.More specifically we do comparison between different control approaches and study theirperformances. Preliminary simulations have shown that MPC is suitable for satellite formationcontrol and can be used to control a formation on an orbit. There are still some problems, morespecifically we have a centralized controller, which is not robust with respect to failures.Validerat; 20160630 (global_studentproject_submitter
Classical Formation Patterns and Flanking Strategies as a Result of Utility Maximization
No full textIn 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.QC 20190125</p
Using Reinforcement Learning to Create Control Barrier Functions for Explicit Risk Mitigation in Adversarial Environments
No full textAir 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.Part of proceedings: ISBN 978-1-7281-9077-8QC 20220503</p
Data-Driven Methods for Enhanced Situation Awareness in Beyond Visual Range Air Combat [Elektronisk resurs]
No full textPilots 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.</p
Enhancing Situation Awareness in Beyond Visual Range Air Combat with Reinforcement Learning-based Decision Support
No full textMilitary 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.</p
A Data-driven Method for Estimating Formation Flexibility in Beyond-Visual-Range Air Combat
No full textTactical 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.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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