Special Issue "AI/Machine Learning in Aerospace Autonomy"

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: closed (31 August 2021).

Special Issue Editor

Prof. Dr. Gokhan Inalhan
E-Mail Website
Guest Editor
Centre for Autonomous and Cyber physical Systems, Cranfield University, Cranfield MK43 0AL, UK
Interests: aeronautical systems; autonomous systems; computing; simulation modelling; defence info systems; operational analysis and simulation; space systems
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Special Issue Information

Dear Colleagues,

As autonomous systems (ASs) become increasingly ubiquitous in complementing and supplementing humans and human-operated aerospace systems, our dependence on them is correspondingly growing. Applications and implementations of ASs within the aerospace domain will soon provide a spectrum of safety-critical, service-critical, and cost-critical functionalities. As such, their through-life evolution, adaptation, resilience, and security to unexpected inputs and events is essential to the trustworthiness of these systems. This dynamic nature of ASs presents us with new challenge frontiers for defining, analyzing, designing, and embedding the aforementioned features into their respective designs. To achieve this complex evolving behavior, the major paradigm shift that we currently face is the transition from design-time automated or sand-boxed autonomous systems to artificial intelligence (AI)-enabled self-aware and learning autonomous systems. AI-enabled autonomous systems within the aerospace domain operate in complex and unpredictable environments, while (a) accomplishing goals while providing through-life resilience and security against anomalies, failures and adversaries; and (b) learning and evolving through diverse experiences, often in contested environments. In that sense, a significant theoretical and methodological leap is required in developing learning-enabled systems within the aerospace domain with trustworthy and assured autonomy.

This Special Issue focuses on novel methods for applying artificial-intelligence-driven autonomy concepts to the design and execution of guidance, navigation, and control algorithms for aerospace vehicles. Topic areas of interest include the design, application, and implementation of AI technologies towards flight control system design, intelligent path/mission planning, sensor/data fusion and perception, situational awareness, classification and reconstruction, goal-based autonomy, multi-agent tactics development, target-task assignment, human–machine teaming, digital twins and data-driven modelling, model-free guidance and control, AI-driven testing and evaluation, AI hardware and software, dynamic verification and validation, and exploring pathways to the qualification and certification of learning-enabled designs.

Prof. Dr. Gokhan Inalhan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • autonomous systems
  • artificial intelligence
  • AI-driven guidance, navigation, and control
  • explainable AI
  • dynamic verification and validation for AI-enabled autonomy
  • digital twins
  • reinforcement learning
  • human–machine teaming
  • model-free learning
  • adversarial learning
  • qualification and certification for AI-enabled autonomy
  • deep learning
  • safety, robustness, adaptation, reconfiguration, resilience and security

Published Papers (1 paper)

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Research

Article
The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial Systems
Aerospace 2021, 8(7), 179; https://doi.org/10.3390/aerospace8070179 - 01 Jul 2021
Viewed by 619
Abstract
Small unmanned aerial systems (UASs) present many potential solutions and enhancements to industry today but equally pose a significant security challenge. We only need to look at the levels of disruption caused by UASs at airports in recent years. The accuracy of UAS [...] Read more.
Small unmanned aerial systems (UASs) present many potential solutions and enhancements to industry today but equally pose a significant security challenge. We only need to look at the levels of disruption caused by UASs at airports in recent years. The accuracy of UAS detection and classification systems based on radio frequency (RF) signals can be hindered by other interfering signals present in the same frequency band, such as Bluetooth and Wi-Fi devices. In this paper, we evaluate the effect of real-world interference from Bluetooth and Wi-Fi signals concurrently on convolutional neural network (CNN) feature extraction and machine learning classification of UASs. We assess multiple UASs that operate using different transmission systems: Wi-Fi, Lightbridge 2.0, OcuSync 1.0, OcuSync 2.0 and the recently released OcuSync 3.0. We consider 7 popular UASs, evaluating 2 class UAS detection, 8 class UAS type classification and 21 class UAS flight mode classification. Our results show that the process of CNN feature extraction using transfer learning and machine learning classification is fairly robust in the presence of real-world interference. We also show that UASs that are operating using the same transmission system can be distinguished. In the presence of interference from both Bluetooth and Wi-Fi signals, our results show 100% accuracy for UAV detection (2 classes), 98.1% (+/−0.4%) for UAV type classification (8 classes) and 95.4% (+/−0.3%) for UAV flight mode classification (21 classes). Full article
(This article belongs to the Special Issue AI/Machine Learning in Aerospace Autonomy)
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