We focus explicitly on multi-agent RL methods in 3D air combat environments, while the survey [4] also includes single-agent RL and 2D dynamics. Several existing works employ techniques that are relevant to multi-agent air combat, such as tactical reward shaping [5], heterogeneous agents [6], attention-based neural networks for situational awareness [7], or communication mechanisms [8] to improve mission strategies. Curriculum Learning (CL) with gradually increasing task difficulty is applied in [9], while enhanced coordination among agents is achieved by adapted training algorithms [10]. The application of HMARL in defense contexts is comparatively limited. An HMARL approach that employs attention mechanisms and self-play is introduced in [11]. Frameworks more closely related to ours appear in [12], [13], with the former integrating CL and the latter employing heterogeneous leader-follower agents together with JSBSim. In this work, we introduce a complex 3D air combat environment and a training framework to learn hierarchical policies using reward shaping and cascaded league-play that gradually increases mission complexity under realistic and heterogeneous conditions. In contrast to prior efforts that are built on established RL algorithms such as Proximal Policy Optimization (PPO) [14], we additionally adapt the recently presented SPO algorithm [3] to the hierarchical multi-agent domain. To the best of our knowledge, this adapted setup has not yet been studied in this context and represents a significant step toward enhancing the realism of such applications.

This is motivated by the strong performance of RL agents in finding effective Courses of Action (CoA) across a wide range of environments, including combinatorial settings such as Chess or Go [1], real-time continuous control tasks found in arcade video games [2], and scenarios that combine control with strategic decision-making, as seen in modern wargames [3]. The application of RL in the context of air combat comes with a number of specific challenges. Those include structural properties of the simulation scenario, such as the complexity of the individual units and their flight dynamics, the exponential size of the combined state and action spaces, the depth of the planning horizon, the presence of stochasticity and imperfect information, etc. Overall the size of the game tree (i.e., the set of possible CoAs) in strategic games and defense scenarios appears vast and beyond the access of straightforward search. Furthermore, real-world operations involve the simultaneous maneuverings of individual units, but also be- ing mindful of the strategic positions and global mission planning. Training policies that integrate real-time control at the troop level with high-level mission planning at the commander level is challenging, as these tasks inherently demand distinct system requirements, algorithmic approaches, and training configurations.

We present CavePerception, a framework for the analysis of sparse data from sensor networks that incorporates elements of inverse modeling and forward modeling. By integrating machine learning with physical modeling in a hypotheses space, we aim to improve the inter-pretability of sparse, noisy, and potentially incomplete sensor data. The framework assumes data from a two-dimensional sensor network laid out in a graph structure that detects certain objects, with certain motion patterns. Examples of such sensors are magnetometers. Given knowledge about the objects and the way they act on the sensors, one can develop a data generator that produces data from simulated motions of the objects across the sensor field. The framework uses the simulated data to infer object behaviors across the sensor network. The approach is experimentally tested on real-world data, where magnetometers are used on an airport to detect and identify aircraft motions. Experiments demonstrate the value of integrating inverse and forward modeling, enabling intelligent systems to better understand and predict complex, sensor-driven events.

Accurately estimating aircraft fuel flow is essential for evaluating new procedures, designing next-generation aircraft, and monitoring the environmental impact of current aviation practices. This paper investigates the generalization capabilities of deep learning models in predicting fuel consumption, focusing particularly on their performance for aircraft types absent from the training data. We propose a novel methodology that integrates neural network architectures with domain generalization techniques to enhance robustness and reliability across a wide range of aircraft. A comprehensive dataset containing 101 different aircraft types, separated into training and generalization sets, with each aircraft type set containing 1,000 flights. We employed the base of aircraft data (BADA) model for fuel flow estimates, introduced a pseudo-distance metric to assess aircraft type similarity, and explored various sampling strategies to optimize model performance in data-sparse regions. Our results reveal that for previously unseen aircraft types, the introduction of noise into aircraft and engine parameters improved model generalization. The model is able to generalize with acceptable mean absolute percentage error between 2\% and 10\% for aircraft close to existing aircraft, while performance is below 1\% error for known aircraft in the training set. This study highlights the potential of combining domain-specific insights with advanced machine learning techniques to develop scalable, accurate, and generalizable fuel flow estimation models.

Precise aircraft recognition in low-resolution remote sensing imagery is a challenging yet crucial task in aviation, especially combat identification. This research addresses this problem with a novel, scalable, and AI-driven solution. The primary hurdle in combat identification in remote sensing imagery is the accurate recognition of Novel/Unknown types of aircraft in addition to Known types. Traditional methods, human expert-driven combat identification and image classification, fall short in identifying Novel classes. Our methodology employs similarity learning to discern features of a broad spectrum of military and civilian aircraft. It discerns both Known and Novel aircraft types, leveraging metric learning for the identification and supervised few-shot learning for aircraft type classification. To counter the challenge of limited low-resolution remote sensing data, we propose an end-to-end framework that adapts to the diverse and versatile process of military aircraft recognition by training a generalized embedder in fully supervised manner. Comparative analysis with earlier aircraft image classification methods shows that our approach is effective for aircraft image classification (F1-score Aircraft Type of 0.861) and pioneering for quantifying the identification of Novel types (F1-score Bipartitioning of 0.936). The proposed methodology effectively addresses inherent challenges in remote sensing data, thereby setting new standards in dataset quality. The research opens new avenues for domain experts and demonstrates unique capabilities in distinguishing various aircraft types, contributing to a more robust, domain-adapted potential for real-time aircraft recognition.

Accurate trajectory prediction (TP) for climbing aircraft is hampered by the presence of epistemic uncertainties concerning aircraft operation, which can lead to significant misspecification between predicted and observed trajectories. This paper proposes a generative model for climbing aircraft in which the standard Base of Aircraft Data (BADA) model is enriched by a functional correction to the thrust that is learned from data. The method offers three features: predictions of the arrival time with 66.3% less error when compared to BADA; generated trajectories that are realistic when compared to test data; and a means of computing confidence bounds for minimal computational cost.

Accurate modeling of aircraft environmental impact is pivotal to the design of operational procedures and policies to mitigate negative aviation environmental impact. Aircraft environmental impact segmentation is a process which clusters aircraft types that have similar environmental impact characteristics based on a set of aircraft features. This practice helps model a large population of aircraft types with insufficient aircraft noise and performance models and contributes to better understanding of aviation environmental impact. Through measuring the similarity between aircraft types, distance metric is the kernel of aircraft segmentation. Traditional ways of aircraft segmentation use plain distance metrics and assign equal weight to all features in an unsupervised clustering process. In this work, we utilize weakly-supervised metric learning and partial information on aircraft fuel burn, emissions, and noise to learn weighted distance metrics for aircraft environmental impact segmentation. We show in a comprehensive case study that the tailored distance metrics can indeed make aircraft segmentation better reflect the actual environmental impact of aircraft. The metric learning approach can help refine a number of similar data-driven analytical studies in aviation.

Computing power, big data, and advancement of algorithms have led to a renewed interest in artificial intelligence (AI), especially in deep learning (DL). The success of DL largely lies on data representation because different representations can indicate to a degree the different explanatory factors of variation behind the data. In the last few year, the most successful story in DL is supervised learning. However, to apply supervised learning, one challenge is that data labels are expensive to get, noisy, or only partially available. With consideration that we human beings learn in an unsupervised way; self-supervised learning methods have garnered a lot of attention recently. A dominant force in self-supervised learning is the autoencoder, which has multiple uses (e.g., data representation, anomaly detection, denoise). This research explored the application of an autoencoder to learn effective data representation of helicopter flight track data, and then to support helicopter track identification. Our testing results are promising. For example, at Phoenix Deer Valley (DVT) airport, where 70% of recorded flight tracks have missing aircraft types, the autoencoder can help to identify twenty-two times more helicopters than otherwise detectable using rule-based methods; for Grand Canyon West Airport (1G4) airport, the autoencoder can identify thirteen times more helicopters than a current rule-based approach. Our approach can also identify mislabeled aircraft types in the flight track data and find true types for records with pseudo aircraft type labels such as HELO. With improved labelling, studies using these data sets can produce more reliable results.

An aviation industry startup accelerator program has selected an artificial intelligence (AI) and natural language processing (NLP)-based platform called Whispr to help improve the speed, accuracy and safety of aircraft inspections and turnarounds. As part of International Airline Group's (IAG) Hangar 51 accelerator program, Whispr is partnering with Spanish carrier Iberia to implement its hands-free voice guidance platform on two projects. The first project is being conducted with Iberia Maintenance at Madrid-Barajas Airport (MAD) to digitize the aircraft inspection documentation process on the airline's new fleet of Airbus A350s, which up until now has been entirely paper-based. According to Hugh O'Flanagan, Whispr's co-founder and CEO, the existing inspection process entails engineers walking around the aircraft and checking tens of items in each different section as they manually complete a paper-based report, which then has to be manually input into a system.