Engagement Decision Support for Beyond Visual Range Air Combat

• URL: http://arxiv.org/abs/2111.03059v1
• Date: Thu, 4 Nov 2021 17:59:45 GMT
• Title: Engagement Decision Support for Beyond Visual Range Air Combat
• Abstract summary: This work aims to provide an engagement decision support tool for Beyond Visual Range (BVR) air combat. In BVR air combat, engagement decision refers to the choice of the moment the pilot engages a target.
• Score: 0.3149883354098941
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work aims to provide an engagement decision support tool for Beyond Visual Range (BVR) air combat in the context of Defensive Counter Air (DCA) missions. In BVR air combat, engagement decision refers to the choice of the moment the pilot engages a target by assuming an offensive stance and executing corresponding maneuvers. To model this decision, we use the Brazilian Air Force's Aerospace Simulation Environment (\textit{Ambiente de Simula\c{c}\~ao Aeroespacial - ASA} in Portuguese), which generated 3,729 constructive simulations lasting 12 minutes each and a total of 10,316 engagements. We analyzed all samples by an operational metric called the DCA index, which represents, based on the experience of subject matter experts, the degree of success in this type of mission. This metric considers the distances of the aircraft of the same team and the opposite team, the point of Combat Air Patrol, and the number of missiles used. By defining the engagement status right before it starts and the average of the DCA index throughout the engagement, we create a supervised learning model to determine the quality of a new engagement. An algorithm based on decision trees, working with the XGBoost library, provides a regression model to predict the DCA index with a coefficient of determination close to 0.8 and a Root Mean Square Error of 0.05 that can furnish parameters to the BVR pilot to decide whether or not to engage. Thus, using data obtained through simulations, this work contributes by building a decision support system based on machine learning for BVR air combat.

Related papers

• FlowATC: Aircraft Trajectory Prediction via Flow Matching [39.046509366232065]
  We present a flow-matching architecture trained exclusively on historical aircraft trajectories, with no route labels or chart supervision.<n>Our model is trained directly on the native, irregular ADS-B sampling interval.<n>We show that the error degrades gracefully with prediction horizon, and the architecture remains effective when retrained on temporally decimated feeds.
  arXiv  Detail & Related papers  (2026-09-15T02:21:06Z)
• Aero-World: Action-Conditioned Aerial Video Generation from Inertial Controls [57.166020875486474]
  We propose textbfAero-World, a method for converting a pretrained image-to-video diffusion model into a controllable aerial video generator.<n>A frozen latent-space Physics Probe, trained independently on real video--IMU pairs, provides differentiable inertial-consistency supervision during LoRA finetuning.<n>On AeroBench, Aero-World improves mean AAS from 57.7 to 63.6 over action-only finetuning and gives a stronger quality-control trade-off than AirScape.
  arXiv  Detail & Related papers  (2026-05-19T12:02:17Z)
• A Fuzzy-Enhanced Explainable AI Framework for Flight Continuous Descent Operations Classification [1.324715593886356]
  Continuous Descent Operations (CDO) involve smooth, idle-thrust descents that avoid level-offs, reducing fuel burn, emissions, and noise.<n>Existing methods in related areas, such as trajectory optimization, lack the transparency required in aviation.<n>This study proposes a Fuzzy-Enhanced Explainable AI framework that integrates fuzzy logic with machine learning.
  arXiv  Detail & Related papers  (2025-08-20T11:08:16Z)
• Deep Learning Based Situation Awareness for Multiple Missiles Evasion [1.7819574476785418]
  We propose a decision support tool to help UAV operators in Beyond Visual Range (BVR) air combat scenarios assess the risks of different options and make decisions based on those. The proposed method uses Deep Neural Networks (DNN) to learn from high-fidelity simulations to provide the operator 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.
  arXiv  Detail & Related papers  (2024-02-07T14:21:21Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning [52.7230652428711]
  Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
  arXiv  Detail & Related papers  (2023-06-05T16:01:33Z)
• Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM) [101.80862265018033]
  Urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as corridors, that link the aerodromes. A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace.
  arXiv  Detail & Related papers  (2021-12-26T14:41:52Z)
• Rethinking Drone-Based Search and Rescue with Aerial Person Detection [79.76669658740902]
  The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today. We propose a novel deep learning algorithm to automate this aerial person detection (APD) task. We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions.
  arXiv  Detail & Related papers  (2021-11-17T21:48:31Z)
• Weapon Engagement Zone Maximum Launch Range Estimation Using a Deep Neural Network [0.3149883354098941]
  This work investigates the use of a Deep Neural Network (DNN) to perform an estimation of the Weapon Engagement Zone (WEZ) maximum launch range.
  arXiv  Detail & Related papers  (2021-11-04T18:20:39Z)
• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
  We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
  arXiv  Detail & Related papers  (2021-07-19T12:54:04Z)
• Distributed Variable-Baseline Stereo SLAM from two UAVs [17.513645771137178]
  In this article, we employ two UAVs equipped with one monocular camera and one IMU each, to exploit their view overlap and relative distance measurements. In order to control the glsuav agents autonomously, we propose a decentralized collaborative estimation scheme. We demonstrate the effectiveness of the approach at high altitude flights of up to 160m, going significantly beyond the capabilities of state-of-the-art VIO methods.
  arXiv  Detail & Related papers  (2020-09-10T12:16:10Z)
• Autonomous and cooperative design of the monitor positions for a team of UAVs to maximize the quantity and quality of detected objects [0.5801044612920815]
  This paper tackles the problem of positioning a swarm of UAVs inside a completely unknown terrain. YOLOv3 and a system to identify duplicate objects of interest were employed to assign a single score to each UAVs' configuration. A novel navigation algorithm, capable of optimizing the previously defined score, is proposed.
  arXiv  Detail & Related papers  (2020-07-02T16:52:57Z)
• Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach [1.552282932199974]
  We propose an autonomous UAV path planning framework using deep reinforcement learning approach. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets.
  arXiv  Detail & Related papers  (2020-03-21T19:33:00Z)
• Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms [98.78553146823829]
  Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks. In this paper, a novel framework is proposed to implement distributed learning (FL) algorithms within a UAV swarm.
  arXiv  Detail & Related papers  (2020-02-19T14:04:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.