The emergence of transformative automated and autonomous technology in both crewed and uncrewed air traffic management systems, including new, sustainable and intelligent aerial platforms, to transport people and goods, represents the next cornerstone in the aerospace industry's on-going evolution.

This is driven by market and industry trends such as the digitalisation of Air Traffic Management (ATM), the explosion of Uncrewed Aerial Systems (UAS) applications in recent years and their integration into crewed aviation airspace. This is creating a significant demand for talented graduates who can help unlock the full potential of Advanced Air Mobility (AAM) applications.

The Advanced Air Mobility Systems MSc is designed to equip you with the skills required to pursue a successful career in transforming the aviation industry, applying the knowledge learned to introduce new automated and autonomous solutions, to enable a safe, orderly and expeditious integrated airspace, where uncrewed aerial systems operate alongside crewed aircraft.

Aerospace at Cranfield

Overview

  • Start dateOctober
  • DurationFull-time one year; part-time up to three years
  • DeliveryTaught modules 40%, group project 20% (or dissertation for part-time students), individual project 40%
  • QualificationMSc
  • Study typeFull-time / Part-time
  • CampusCranfield campus

Who is it for?

This course provides engineering, physics, computing, or mathematics graduates with advanced skills which can be applied to aviation, drone, security, defence, and aerospace industries. We also welcome students with industry experience and offer the course on a part-time basis for those looking to work whilst continuing in employment.

Why this course?

The Advanced Air Mobility Systems MSc is designed to equip you with the skills required to pursue a successful career in transforming the aviation industry, applying the knowledge learned to introduce new automated and autonomous solutions to improve the industry as a whole.

Taught through a unique combination of theoretical and practical-based sessions, you will cover subjects in ATM, Uncrewed Traffic Management (UTM), enabling sensor infrastructure (communications, navigation, surveillance), sensor fusion and artificial intelligence for autonomous systems. The MSc course content has been based on advice from the Industrial Advisory Board, comprising industrial representatives from big primes to small- and medium-sized enterprises. The Industrial Advisory Board also recommend thesis and project topics ensuring their real-world relevance, another effective differentiator in the job market. This allows you to familiarise yourself with companies from the Industrial Advisory Board and be exposed to their research interests, paving the way for potential job opportunities.

The Cranfield MSc in Advanced Air Mobility Systems is unique in that it offers a combination of subjects much sought-after in the aviation, air traffic, and drone industries, that are not covered in a single MSc course anywhere else, giving particular emphasis to the digitalised integrated architecture, the enabling sensor infrastructure (including communication, navigation, and surveillance) and intelligent algorithms, such as flight management and planning, and deconfliction. As a successful graduate of this MSc course, you will become conversant in key aspects of automation and autonomy in emerging crewed/uncrewed traffic management which places you at an advantage in today's competitive employment market.

A key feature of the MSc is the inclusion of a CAA approved UAV remote pilot competence course. The course incorporates a ground school element for flight planning – covering principles of flight, rules and regulations of the air, using aviation charts, risk assessment and meteorology – and flight training to provide basic pilot competence, including how to respond in an emergency and being able to operate safety features. Successful completion of the course allows students to fly small UAV’s in the Open Category.

Informed by industry

The MSc course content has been based on advice from the Industrial Advisory Board (IAB), comprising industrial representatives from big primes to small- and medium-sized enterprises. The relevant, competent and pro-active Industrial Advisory Board includes:

Boeing UK Connected Places Catapult
Thales Spirent
BAE Systems ANRA Technologies
NATS Heathrow
SAAB QinetiQ
FlugAuto General Atomics Aeronautical Systems UK
Rolls-Royce
 Blue Bear Systems Research Ltd.
Lockheed Martin UK Northrop Grumman
QuadSAT HEROTECH8
Altitude Angel Indra UK

 

Members of the Board not only continuously advise on updating the course content but also provide topics for individual research projects (IRPs). After the final oral exams in early September, all students present posters summarising their IRPs to the whole Industrial Advisory Board, thus exposing their work to seasoned professionals and potential employers. The IRPs benefit from our own lab where real autonomous vehicles can be designed and tested.

Course details

In this section
Overview
Delivery
Modules
Teaching team

The MSc course consists of three weighted components, taught modules, and individual research project, and a group project. The taught course element includes eight taught compulsory modules, generally delivered from October to March. The eight modules cover the fundamentals of Air Traffic Management (ATM) and communications systems and progresses to the core subjects of AI for autonomous systems and Uncrewed Traffic Management (UTM).

The taught part of the course is followed by a Group Design Project (GDP) and individual research projects (IRPs). The GDP enables students to work as part of a team, develop project planning and management skills, and communications abilities, to design, implement, validate and test an advanced air mobility system component, applying the knowledge acquired in the taught modules and integrate the various methods learned.

Students are also supported in their learning and personal development through participation in: industry seminars, group poster sessions, group discussions, group presentations, video demonstrations, case studies, laboratory experiments, coursework, and project work. Students will receive hands-on experience accessing equipment and facilities within our Digital Aviation research and Technology Centre  and Aerospace Integration Research Centre.

Course delivery

Taught modules 40%, group project 20% (or dissertation for part-time students), individual project 40%

Group project

The group design project facilitates the design, build, and operation of autonomous solutions for the emerging Advanced Air Mobility Systems market, modernised and integrated crewed/uncrewed Traffic Management, thus integrating and applying the knowledge students acquire in the taught modules. The group design project also aims to provide students with experience of working on a collaborative engineering project, within an industry structured team, developing transferable skills that include working in a team with members having diverse backgrounds and expertise, project management, and technical presentations.

Part-time students are encouraged to participate in a group project as it provides a wealth of learning opportunities. However, an option of an individual dissertation is available if agreed with the Course Director.

Individual project

Our industry partners sponsor individual research projects allowing you to choose a topic that is commercially relevant and current. Topics are chosen during the first teaching period in October and you begin work during the second half of the MSc course (May-August). The project allows you to delve deeper into an area of specific interest, taking the theory from the taught modules and joining it with practical experience.

Projects encompass various aspects of operations, not only concerned with design but including civil applications, architectures, systems, sensors, and other feasibility studies industry wishes to explore.

For the duration of the project, each student is assigned both a university and industry supervisor. In recent years, students have been based at sponsor companies for sections of their research and have been given access to company software/facilities.

During the thesis project, all students give regular presentations to the course team and class, which provides an opportunity to improve your presentation skills and learn more about the broad range of industry-sponsored projects.

For part-time students, it is common that their research thesis is undertaken in collaboration with their place of work.

Modules

Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only, and may be subject to change for your year of entry.


Course modules

Compulsory modules
All the modules in the following list need to be taken as part of this course.

Induction

Module Leader
  • Professor Antonios Tsourdos
Aim
    The aim of this module is to introduce the MSc course to the students, to make the students aware and familiarize with the facilities, resources and support available.
Syllabus
    • Advanced Air Mobility MSc course,
    • Introduction to Student and Academic Support,
    • Use of library services,
    • Essential research skills,
    • Use of Career Development Service,
    • Introduction to Cranfield University Virtual Learning Environment.
Intended learning outcomes

On successful completion of this module you will be able to:

  • Recognise the requirements and expectations of a Cranfield MSc degree in terms of critical thinking, independent working and team working,
  • Recognise the need to maintain high level of health and safety, and to demonstrate good health and safety practice throughout their study and research at Cranfield,
  • Recognise ethical behaviour such as avoiding plagiarism,
  • Effectively use library resources and the Cranfield Virtual Learning Environment in supporting their study, and other online resources,
  • Recognise the different formats and expectations of assessment and effectively use the available feedback mechanisms.

Introduction to Advanced Air Mobility

Module Leader
  • Professor Antonios Tsourdos
Aim
    The aim of this module is to provide an overview of the course and to introduce the main aspects of Advanced Air Mobility (AAM) and Autonomous Systems underpinning the course, including Systems Engineering principles, safety and regulatory considerations.
Syllabus
    • Overview of current ATM, UTM, and UAM ecosystems,
      1. Overview of the different architectures,
      2. Airspace structures and classifications,
    • Overview of enabling technologies and systems,
      1. Communication,
      2. Navigation,
      3. Surveillance,
    • Systems Engineering Principles,
    • Safety and Regulatory Context,
    • Ethical considerations of uncrewed and autonomous systems.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Contrast the main practical applications of AAM (including Uncrewed traffic Management (UTM) and Urban Air Mobility [UAM]) and define their engineering subsystems,
  • Evaluate the main engineering challenges of AAM analysis and design,
  • Analyse qualitatively the functions and capabilities of the main subsystems of AAM,
  • Debate the ethical concerns and regulatory challenges concerning uncrewed and autonomous air traffic operations.

Statistical Learning Methods

Aim
    This module aims to equip you with practical knowledge in statistics required for assessment and quantification of uncertainties in real life scenarios of data analysis. Module presents practically important algorithms of statistical learning for both prediction and decision making purposes and provides the opportunities for your experimental evaluation during the lab sessions. Tools for evaluation of learning algorithms’ performance are also considered and implemented to practical examples.
Syllabus
    • Introduction to statistical learning,
    • Statistics fundamentals: probability, random variables, descriptive statistics and stochastic processes,
    • Statistical inference: estimation and testing, evaluation metrics,
    • Bayesian methods: Naïve Bayes and Bayesian Networks,
    • Markov processes and chains, Kalman estimators,
    • Statistical modelling and decision making: regression, mixture models and classification approaches,
    • Case study: application of statistical learning for aerospace sector problem.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Relate statistical techniques for uncertainty quantification to real life problems,
  • Differentiate experimental data according to the underlying models of stochastic processes,
  • Propose statistical learning methods suitable for particular problem,
  • Assess the outcomes of the statistical learning.

Air Traffic Management Systems

Aim

    The aim of this module is to provide an understanding of the current and future air traffic management (ATM) and air traffic control (ATC) systems, their functional architectures, main algorithms and applications. Both the regulatory and technical context will be explained, with an emphasis towards ATM digitalisation and increased automation. The module also aims to discuss current ATM standards and technology applied in the systems and to review the future concepts as described by SESAR/NextGen. Finally, an overview of the appropriate tools to develop and assess ATM components will be presented, enabling students to critically evaluate the performance of an ATM function.
Syllabus

    Overview of current ATC systems:

    • ASM airspace structures, airspace segregation, conditional routes, cross border management, civil-military coordination, and route network management,
    • ATFM, traffic flow network management, Demand/Capacity Balancing procedures, Flow control at pre-tactical and tactical levels,
    • ATC architecture, Decision support tools (DSTs), Controller working position (CWPs) characteristics,
    • CNS: ATM requirements in Communication, Navigation and Surveillance systems.
    • ATC Operational procedures at the different environments (airports, terminal and en route airspace),
    • ATM as a sociotechnical system,
    • Safety, ethical and regulatory considerations.

     

    Future Air Navigation System:

    • FANS Communication - required communication performance (RCP), AMSS, VDL, SSR Mode S,
    • FANS Surveillance – required surveillance performance (RSP), ADS/ADS-B, SSR Mode S,
    • FANS Navigation - Required Navigation Performance (RNP), Area Navigation (RNAV), GNSS and its augmentations, RNP/RNAV.

     

    Air Traffic Management Key Performance Indicators:

    • Capacity and delay models of airport and air routes,
    • The ATM cost model,
    • The ATM efficiency model.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Examine the current and future ATM ecosystem and its Institutional, Operational and Technical enablers as proposed in SESAR/NextGen programmes,
  • Appraise the airspace organisation and classification and separation standards,
  • Critically evaluate the current ATM components (Airspace Separation Management (ASM), Air Traffic Flow Management (ATFM) and Air Traffic Control),
  • Formulate systems engineering approaches to the development of ATM components to meet future air traffic demands,
  • Analyse the performance of ATM systems, in a simulation environment using corresponding performance metrics.

Aerial Communications Systems

Module Leader
  • Professor Saba Al-Rubaye
Aim
    This module aims to provide you with new skills and understanding of Aeronautical communications design and overview of current approaches to line of sight and beyond line-of-sight techniques.
Syllabus

    Overview of airborne communications:

    • Radio communication systems,
    • Communications standard,
    • Communications technology (4G/5G).

     

    Aeronautical Communication:

    • Airspace integration UAS/UAM/ ATM/ATC Technologies,
    • Digital Aeronautical Communications System,
    • BLOS and Satellite Data Link Connectivity.

     

    Communications system design:

    • Analogue and Digital Modulation Schemes,
    • Multiplexing and Transmission Techniques,
    • Techniques (e.g., Uplink/Downlink Model, Noise, SNR),
    • Link budget analysis.

     

    Antennas design:

    • Propagation,
    • Gain and Polarization.

     

    Communications Networking:

    • Topology techniques,
    • Mobility and ad hoc networking.

     

    Case study:

    • An application for UAM/Drone air to ground connectivity.

     
Intended learning outcomes

On successful completion of this module you should be able to:

  • Distinguish the fundamental principles of airborne communication systems,
  • Categorise different practices and procedures that is essential for air to ground communications,
  • Estimate link budget analysis & sustainable mobility system design,
  • Assess different antenna design and propagation aspect for Line-of-sight/Beyond visual LOS (LOS/BLOS),
  • Evaluate security and networks techniques.

 

Uncrewed Traffic Management

Module Leader
  • Professor Antonios Tsourdos
Aim
    The aim of this module is to provide you with an understanding of the advanced air mobility ecosystem, its functional architectures, main algorithms and applications in the context of unmanned traffic management (UTM), urban air mobility (UAM) and autonomous vehicles. Both the regulatory and technical context will be explained. The module aims also to give you a thorough understanding of the appropriate tools to develop and deploy automated and autonomous systems in airspace management, enabling them to critically evaluate the performance of a particular architecture, enabling sensor, algorithm or service.
Syllabus

    Detailed analysis of current UTM ecosystem

    • Architectures and Concept of Operations,
    • Flight rules, airspace structures and classifications, air traffic service.

     

    Definition of UTM Services

    • Identification and tracking – registration, e-identification, tracking, surveillance data exchange,
    • mission management – Operation plan preparation/optimisation and processing, risk analysis assistance, dynamic capacity management,
    • conflict management – strategic and tactical conflict detection and resolution,
    • airspace management – geo-awareness and geo-fencing, drone aeronautical information management,
    • interface with Air Traffic Control and manned aviation.

     

    Detailed analysis of Enabling Technologies

    • Centralised and decentralised communication, navigation and surveillance systems.

       

      Detailed analysis of planned UAM ecosystem

    • Architectures and Concept of Operations,
    • Flight rules, airspace structures and classifications, air traffic service.
Intended learning outcomes

On successful completion of this module a you should be able to:

  • Distinguish the emerging UTM and UAM enabling infrastructure, including navigation, surveillance sensors and automated systems,
  • Define and explain the regulatory and technical challenges of UTM and UAM (e.g. separation standards, conflict avoidance, automation),
  • Interpret the functional, technical, safety and regulatory targets for safe implementation of AAM applications,
  • Critically evaluate the different UTM and UAM ecosystems, their individual components and their related functions and services,
  • Analyse the performance of UTM and UAM systems, in a simulation environment using corresponding performance metrics.

Data Analytics and Visualisation

Module Leader
  • Dr Ivan Petrunin
Aim
    This module will introduce students to data analytics, overview challenges and solutions in this area, present approaches to predictive and descriptive data mining and explain unsupervised learning techniques suitable for new information discovery. Visualisation tools and performance metrics are also considered within the module. You will benefit from knowledge of basic concepts of statistics for performance assessment and evaluation.
Syllabus
    • Introduction to Data Analytics,
    • Data exploration and pre-processing,
    • Predictive analytics: regression and classification methods,
    • Clustering and dimensionality reduction,
    • Graph analysis and visualisation,
    • Software and tools for data analytics,
    • Case study: application of data analytics techniques and visualisation tools for knowledge discovery problem.

     
Intended learning outcomes

On successful completion of this module you should be able to:

  • Distinguish stages of the data analytics workflow,
  • Categorize data analysis and visualisation techniques with respect to data analytics stages,
  • Plan data analytics workflow based on the available data and formulated requirements,
  • Set up algorithms for discovery of new information from the large data sets,
  • Evaluate performance of the algorithms and quality of the data analysis outcomes.

Artificial Intelligence for Autonomous Systems

Module Leader
  • Dr Ivan Petrunin
Aim
    The aim of this module is to introduce you to the Artificial Intelligence algorithms suitable for real life problems concerning the Autonomous Systems (AS): target detection, identification, recognition and tracking using multiple heterogeneous sensors from cooperating AS, including accuracy assessment and uncertainty reduction for these applications
Syllabus
    • Introduction to AI for AS with overview of AS sensors and imaging,
    • AI Algorithms: Unsupervised Learning,
    • Unsupervised Learning – Lab session,
    • AI algorithms: Supervised Learning – SVM and Neural Networks,
    • Supervised Learning – Lab session,
    • AI Algorithms: Supervised Learning – Deep Neural Networks,
    • Deep Learning – Lab session,
    • Automated Reasoning,
    • Case Study: AI for AS.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Categorize AI methods for real-life scenarios of Autonomous Systems (AS) applications,
  • Assess Applicability of Artificial Intelligence (AI) algorithms for AS,
  • Set up the commonly used AI algorithms for application in the AS context,
  • Evaluate performance of AI algorithms for a typical AS application in a simulation environment.

 

Guidance and Navigation for Autonomous Systems

Aim
    In modern autonomous systems, it is essential to design an appropriate guidance and navigation system. Therefore, this module aims to deliver not only fundamental and critical understanding of classical and advanced guidance and navigation theories, but also evaluation of their nature, purposes, pros and cons, and characteristics. This should enable you to critically select and design appropriate guidance and navigation for their specific autonomous systems.
Syllabus
    • Introduction on navigation and guidance systems;
    • Path planning for autonomous systems
    • Path following for autonomous systems
    • UAV (Unmanned Aerial Vehicle) guidance systems;
    • Guidance approaches: conventional guidance such as PN (Proportional Navigation), geometric guidance, and optimal guidance;
    • Navigation approaches: navigation systems, GNSS (Global Navigation Satellite System), terrain based navigation, SLAM (Simultaneous Localisation and Mapping);
    • Cooperative guidance and collision avoidance.
Intended learning outcomes

On successful completion of this module you should be able to:

  1. 1. Critically understand the fundamentals of the various guidance techniques and their properties.
  2. 2. Describe the algorithms that are required to produce an estimate of position and attitude,
  3. 3. Describe the characteristics, purposes, and design procedures of guidance and navigation systems.
  4. 4. Evaluate challenging problems in the guidance and navigation approaches for autonomous systems.
  5. 5. Describe the challenging issues of the cooperative guidance design and critically evaluate the cooperative guidance systems to be able to enhance the overall performance,

Teaching team

You will be taught by Cranfield's experienced academic staff. Our staff are practitioners as well as tutors, with clients which include the members of the Industrial Advisory Board and beyond. Knowledge gained working with our clients is continually fed back into the teaching programme, to ensure provision of durable and transferrable skills practised on problems relevant to industry. Additionally, experienced members of the Industrial Advisory Board deliver industrial seminars in which they share their experience and explain the research and development proprieties of their companies. The Course Director for this programme is Dr Hae In Lee.

Dr Hae In Lee

Lecturer in Autonomous Systems
Antonios Tsourdos

Professor Antonios Tsourdos

Head of the Autonomous and Cyber-Physical Systems Centre
Saba Al-Rubaye

Professor Saba Al-Rubaye

Professor Chair in Telecommunications and Autonomous Systems
Ivan Petrunin

Dr Ivan Petrunin

Reader in Signal Processing for Autonomous Systems and DARTeC Fellow
Hyo-sang Shin

Professor Hyo-sang Shin

Professor in Guidance, Navigation and Control

It is becoming clear that Advanced Air Mobility [encompassing subject areas such as digitalization of Air traffic Management (ATM), Unmanned Aircraft Systems Traffic Management (UTM) and Urban Air Mobility (UAM)] will be a major transformative factor in the aerospace, defence, and security sectors.
In the UK, and wider in the EU, we perceive a shortage of qualified people trained in Advanced Air Mobility (AAM), specifically in autonomy and automation in ATM, UTM and UAM. In particular, we would need not only engineers but also software and application developers with a deep understanding of the AAM subject areas described above, tailoring them to tackle ambitious industrial problems of enabling ubiquitous UAS operations and their seamless integration into conventional manned aviation.
We need employees able to address complex, real-world problems, who have state-of-the-art ATM, UTM and UAM expertise, and who are equipped to work collaboratively across traditional disciplinary boundaries. This rounded skillset is of high importance to us, and the MSc course in AAM is perfectly suited to fill this gap.

Malin Svahn

Director UK Innovation Hub, Saab Technologies UK Ltd

Your career

The industry-led education that you will receive on this MSc will place you among the most desirable candidates for recruitment into global enterprises through to smaller innovative start-ups looking for the brightest talent. Industrial contact may take place even from the Individual Research Project that enables familiarisation with our Industry Advisory Board.

Graduates from the MSc have gone into roles including:

  • Autonomous systems engineer
  • Design engineer
  • Research Assistant in Advanced Air Mobility
  • Applied Vision Control (KTP Associate)

Companies that employ our graduates include:

  • BAE Systems
  • Thales
  • SAAB
  • Boeing
  • NATS
  • Heathrow Airport
  • Inmarsat

As a graduate from this course you will be equipped with the advanced skills which could be applied to the aviation, air traffic, air transport, security, defence, and aerospace industries. This approach offers you a wide range of career choices in industry, and some decide to continue their education through PhD studies available within Cranfield University or elsewhere.

Cranfield’s Career Service is dedicated to helping you meet your career aspirations. You will have access to career coaching and advice, CV development, interview practice, access to hundreds of available jobs via our Symplicity platform and opportunities to meet recruiting employers at our careers fairs. Our strong reputation and links with potential employers provide you with outstanding opportunities to secure interesting jobs and develop successful careers. Support continues after graduation and as a Cranfield alumnus, you have free life-long access to a range of career resources to help you continue your education and enhance your career.

 

Part-time route

We welcome students looking to enhance their career prospects whilst continuing in full-time employment. The part-time study option that we offer is designed to provide a manageable balance that allows you to continue employment with minimal disruption whilst also benefiting from the full breadth of learning opportunities and facilities available to all students. The University is very well located for visiting part-time students from all over the world and offers a range of library and support facilities to support your studies.

As a part-time student you will be required to attend teaching on campus in one-week blocks, for a total of 9 blocks over the 2-3 year period that you are with us. Teaching blocks are typically run during the period from October to March, followed by independent study and project work where contact with your supervisors and cohort can take place in person or online. Students looking to study towards the MSc will commence their studies in the October intake whereas students who opt for the research-based MRes may commence either in October or January.

We believe that this setup allows you to personally and professionally manage your time between work, study and family commitments, whilst also working towards achieving a Master's degree.

How to apply

Click on the ‘Apply now’ button below to start your online application.

See our Application guide for information on our application process and entry requirements.

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