AESS Avionics Systems Panel Research and Education Perspectives

  • Part-I: Avionics Systems Research and Innovation Opportunities
  • Part-II: Avionics Systems Educational Needs and Curricular Evolution

This tutorial is being presented on behalf of the IEEE Aerospace Electronic Systems Society (AESS) Avionics Systems Panel (ASP). The panel comprises experts in various areas of Avionics Engineering representing industry, academia, and the government around the globe. The tutorial forms part of efforts undertaken by the panel to propagate expertise in Avionics Systems. The tutorial is configured in two distinct segments, which in turn are interconnected as avionics (1) research and (2) education.

Part one (avionics research) elucidates the contemporary and future, industry focused, development and innovation areas in the field of Avionics Engineering. The constantly increasing density of air traffic and the growing diversity of aerospace vehicles that will occupy the air space imposes new requirements on Communication, Navigation, Surveillance/Air Traffic Management (CNS/ATM) and Avionics (CNS+A) technologies. Unmanned Aerial Systems (UAS) are key drivers in the evolution of CNS+A systems. Additionally, Urban Air Mobility (UAM) is expected to add a new dimension to future aviation related technologies. The conventional ATM network and services will be expanded to include new UAS and Space Traffic Management (UTM/STM) schemes for un-segregated operations of manned and autonomous vehicles both in atmospheric flight (including low-level and urban operations) and in near-Earth space operations. In the wake of UTM/UAM come further advances in Performance-Based Operations (PBO), which will have profound impacts on aviation equipment mandates and standards, with tangible benefits in terms of airspace capacity, safety, access modalities, prioritization and overall fairness. Another key area will be the design of Human-Machine Interfaces and Interactions (HMI2) supporting trusted autonomous operations (i.e., human-autonomy teaming). All these spheres will utilize Machine Learning & Artificial Intelligence (ML/AI) algorithms to enhance the overall CNS+A systems performance and efficiency.

Likewise, certification of ML/AI in aviation and especially safety critical avionics is a major focus of current research. Proliferation of cyber-physical systems, especially for UTM/UAM operations makes cyber security a critical requirement.

The aim of Part two of this tutorial (avionics education) is to discuss practical approaches for the alignment of educational curricula with that of relevant industry needs and technological advances in the field of avionics engineering. A review of existing avionics programs will be presented, highlighting their current shortcomings vis-a-vis prevailing industry requirements and future trends covered in part one. Building on the fact that most curricula have not been updated since the late 1990s, a new and comprehensive undergraduate curriculum is proposed, clarifying the rationale of variations from current practices. Additionally, a curriculum structure suitable for graduate avionics programs is presented, with a focus on specialist skills aligned with the prevailing research and innovations areas in Avionics Engineering. The overall objective of the proposed curricula is to bridge the gaps between higher education, industry practices, government regulators, and public stakeholder needs; towards maximizing educational outcomes and preparedness of the avionics engineering workforce, and to tackle some of the most important challenges and opportunities faced by the aerospace sector globally.

Efficient and Large-Scale Air Traffic Data Analysis with OpenSky

This tutorial introduces the OpenSky Network (, a community-based receiver network which continuously collects air traffic surveillance data and makes it accessible to researchers for free. Using a global network of over 4000 Mode S and ADS-B receivers, the network has collected and provides over 2 PB of surveillance data. This data has been used by academics, authorities and companies around the world for their research, resulting in more than 200 peer-reviewed publications to date. Martin and Xavier will provide an overview on how the OpenSky Network works and which data is available. Moreover, using several case studies, they will demonstrate what kind of analyses are possible and how to use OpenSky’s data set for your research. In particular, they will show how to use the traffic library in order to use the large amount of air traffic data provided by OpenSky conveniently and efficiently.

Reliable Navigation for Unmanned Aircraft Systems

This course provides a fundamental background in assured navigation for unmanned aircraft systems (UAS). It first introduces the various UAS/RPAS application domains and operational environments, UAS flight management and path planning, required performance parameters, and autonomy at the various levels of the Guidance, Navigation and Control function. Furthermore, it addresses the foundations of Global Navigation Satellite Systems (GNSS) and inertial navigation and discusses the challenges of operating in the various target environments with sole-means GNSS. Next, augmentation methods and alternative navigation methods will be discussed with a focus on guaranteeing required navigation performance in, especially, GNSS-challenged environments. Finally, the course will talk about the role of the navigation function in surveillance, geo-fencing and relative navigation in case of swarms of UAS.

Intelligent Control Architecture for Autonomous Vehicles

The use of remotely-operated vehicles is ultimately limited by economic support costs, and the presence and skills from human operators (pilots). Unmanned craft have the potential to operate with greatly reduced overhead costs and level of operator intervention. The challenging design is for a system that deploys a team of Unmanned Vehicles (UVs) and can perform complex tasks reliably and with minimal (remote) pilot intervention. A critical issue to achieve this is to develop a system with the ability to deal with internal faults, and changes in the environment as well as their impact on sensor outputs used for the planning phase.

The tutorial objective is to present step by step the development process (from requirements to prototyping) of an Intelligent Vehicle Control Architecture (IVCA) that enables multiple collaborating UVs to autonomously carry out missions. The architectural foundation to achieve the IVCA lays on the flexibility of service-oriented computing and agent software technology. An ontological database captures the remote pilot skills, platform capabilities and, changes in the environment. The information captured (stored as knowledge) enables reasoning agents to plan missions based on the current situation. The combination of the two above paradigms makes it possible to develop an IVCA that is able to dynamically reconfigure and adapt itself in order to deal with changes in the operation environment. The ability to perform on-the-fly re-planning of activities when needed increases the chance to succeed in a given mission. The IVCA realization is underpinned by the development of fault-tolerant planning and spooling modules (fault diagnosis and recovery) as well as a module called matchmaker to link services with available capabilities.

The IVCA is generic in nature and can be easily adapted to UVs from different domains (i.e. land, water, and air/space). However, the IVCA aims at a case study where Unmanned Marine Vehicles (UMVs) are required to work cooperatively. They are capable of cooperating autonomously towards the execution of complex activities since they have different but complementary capabilities. The above UMV configuration, where the marine robots are tasked to autonomously do mission works before recovery, is possible at a cost of endowing the UMVs with “intelligence” that in former solutions is provided by remote or even in-situ human pilots.

The IVCA development applies the software/systems engineering principles. The tutorial is structured in four parts. Part I (background) consists of a brief review of technologies related to the IVCA and a comparison of control architectures for autonomous UVs. Part II (requirements analysis and design) entails the user and system requirements, and the system architecture specification/design. Part III (implementation and integration) describes the IVCA realization based on Robot Operating System (ROS) for the above case study. Session IV (verification and validation) deals with the evaluation of the IVCA by means a simulation.

Detect and Avoid for Unmanned Aircraft Systems

In the latest years, sense and avoid (SAA), or detect and avoid (DAA), has represented one of the main roadblocks to the integration of unmanned aircraft systems (UAS) operations. This course outlines and reviews architectures, technologies, and algorithms for SAA. First, starting from a discussion about what constitutes a UAS and how it is different than manned aircraft, basic SAA definitions and taxonomies are discussed. Ground-based/airborne and cooperative/non-cooperative architectures are covered. The SAA process is dissected into its fundamental tasks, which are discussed in details. Different sensing algorithms and technologies are presented, including radar and optical systems. Potential and challenges of multi-sensor-based systems and data fusion are pointed out. Techniques for conflict detection, and approaches for remotely operated or autonomous avoidance are introduced. The tutorial ends with an overview of current perspectives and recent progress relevant to SAA for UAS integration in the Air Traffic Management (ATM) system and in the framework of UAS Traffic Management (UTM) / U-Space and Urban Air Mobility.

Aviation Cyber-Security Regulation: The DO-326/ED-202-Set Practical Guidance & Considerations

The “DO-326/ED-202 Set” provides regulatory “Guidance & Considerations” for the certification & in-service continued airworthiness for cyber-security aspects.
This course provides information necessary to help minimize DO-326/ED-202-set compliance risks and costs, while also optimizing cyber-security levels for the development, deployment & in-service phases. The instructor will guide attendees through topics such as aircraft security aspects of safety, systems-approach to security, security planning, the airworthiness security process, and security effectiveness assurance. The entire ecosystem of aviation avionics software development will be revisited to include the DO-326/ED-202-Set as a new, integral member of the “classic” safety-oriented development process including the SAE standards ARP-4761 for Safety & ARP-4754A for Systems Development, and software & Hardware development standards DO-178C & DO-254, respectively.

Attendees may include managers, engineers, quality assurance, certification personnel – as well as aircraft manufacturers, operators, maintainers, service providers and other aviation stakeholders, who need to prepare for Cyber-Security regulatory compliance of their aircraft/systems/organizations.

Aviation Cyber-Security Regulation: Introduction to the DO-326/ED-202-Set

The international standards D-326A (U.S.) and ED-202A (Europe) titled “Airworthiness Security Process Specification” are the cornerstones of the “DO-326/ED-202 Set”: the only Acceptable Means of Compliance (AMC) by FAA & EASA for aviation cyber-security airworthiness certification, as of 2019. The “DO-326/ED-202 Set” also includes companion documents DO-356A/ED-203A: “Airworthiness Security Methods and Considerations” & DO-355(A)/ED-204(A): “Information Security Guidance for Continuing Airworthiness” (U.S. & Europe) and ED-201: “Aeronautical Information System Security (AISS) Framework Guidance” & ED-205: “Process Standard for Security Certification / Declaration of Air Traffic Management / Air Navigation Services (ATM/ANS) Ground Systems“ (Europe only).

This 3-hour fast-paced course will introduce attendees to the background, structure, basic concepts and essential practices of this new, unavoidable set of standards.


Attendees may include managers, engineers, quality assurance, certification personnel – as well as aircraft manufacturers, operators, maintainers, service providers and other aviation stakeholders, who need to prepare for Cyber-Security regulatory compliance of their aircraft/systems/organizations.

Machine Learning for Avionics

In this course we will explore the term machine learning and define algorithms to be generally considered as machine learning. The course is built around use cases where machine learning can provide advantage in form of time and cost savings. We are going to link the use of machine learning to existing algorithms used for system diagnostics which include signal processing algorithms, feature extraction and classification methods. The tutorial will begin with Signal to Noise Ratio, variance, Standard Deviation and FFT which can be used for unsupervised, supervised and reinforcement learning where such as regression, k-nearest neighbors and other algorithms are used. The tutorial will also introduce the basics of the neural networks, their design and pros and cons with explanation why certification authorities do not accept systems using neural networks for safety critical applications. The tutorial will be concluded by a use case utilizing machine learning with data classification algorithms for automatic recurrent testing of avionics software modifications.

Artificial Intelligence / Autonomous Systems and Human Autonomy Teaming

The course is designed to appeal to scientific and engineering professionals who wish to obtain and or increase knowledge in Artificial Intelligence / Autonomous Systems and Human Autonomy Teaming. Introduction to the main foundational concepts and techniques used in Artificial Intelligence (AI); including decision making, planning, machine learning, and cognition. Includes a range of real-world applications in which AI is currently used in aeronautical and aerospace systems. Presentation of theoretical concepts occurs. Systematic study of methods and research findings in the field of human perception, with an evaluation of theoretical interpretations. Provides a basis for the understanding of these perceptual capabilities as components in Artificial Intelligence in aviation/aerospace systems. The field of human-autonomy teaming (HAT) is fast becoming a significant area of research, especially in aviation. HAT is highly interdisciplinary, bringing together methodologies and techniques from robotics, artificial intelligence, human-computer interaction, cognitive psychology, neuroscience, neuroergonomics, and other fields. The topics covered will include technologies that enable human-machine interactions, the psychology of interaction between people and machines, how to design and conduct HAT studies, and real-world applications such as assistive machines. Covered are the advanced systematic study of methods and research findings in the field of human and computer perception, with an evaluation of theoretical interpretations. Algorithmic foundations of AI / ML. Additionally, introduction to Autonomous Systems will be covered. Surveys the fundamentals of autonomous aircraft system operations, from sensors, controls, and automation to safety procedures, human factors. Presentation of advanced theoretical concepts for artificial intelligence in the areas of knowledge representation and search techniques. The concept of the perceptron and neuron will be covered along with 1st, 2nd, and 3rd generation neural networks. Machine Learning is also covered: hands-on, live and in-action machine learning problems will be solved: utilizing regression analysis, ANNs, RNNs, CNNs (Deep Learning), SNNs, RELs, SVMs, and Bayesian Belief Networks. This course presents the latest major commercial uses of UAS, and manned aircraft that will be going from 2-pilot operations to 1-pilot operations to unmanned operations.

Introduction to Aviation Cyber Security

The cyber threat landscape of aviation is every increasing. Particularly concerning are those threats that bring novel risks that are specific to aviation and are perceived to impact public safety and well-being. This tutorial will introduce you to aviation cyber security, focusing on the aircraft being the center of an increasingly complex, technology-driven aviation ecosystem. Upon completion of this tutorial, you will be able to comprehensively summarize and skillfully analyze today’s aviation cyber security landscape including both manned and unmanned aircraft. You will be able to differentiate real vs. perceived as well as emerging vs. future threats. You will be able to recall aviation and cyber security terminology, explain cyber security essentials, and illustrate how cyber security applies to the passenger carrying aircraft, unmanned aircraft, and their supporting systems. Using examples and case studies, you will be able to evaluate threats from vulnerabilities as well as risks from threats to these systems. You will be able to recognize, examine, and compare some of the state-of-the-art and recent advances in aviation cyber security, including those related to avionics, crew, and aircraft, air traffic control, UAS, and UTM systems.