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 along side 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.

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 students to familiarise themselves with companies from the Industrial Advisory Board and be exposed to their research interests, paving the way for potential job opportunities.

This course 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 (incl. communication, navigation, and surveillance) and intelligent algorithms, such as flight management and planning, and deconfliction. Successful graduates of our MSc course become conversant in key aspects of automation and autonomy in emerging crewed/uncrewed traffic management which places them 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
  • Blue Bear Systems Research Ltd.
  • Rolls-Royce
  • Lockheed Martin UK
  • Northrop Grumman
  • QuadSAT
  • HEROTECH8

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 sensor fusion, guidance and navigation, 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.

Introduction to Advanced Air Mobility

Aim
    The aim of this module is to provide you with 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, UAM and UTM ecosystems
      • Overview of the different architectures
      • Airspace structures and classifications
    • Overview of enabling technologies and systems
      • Communication
      • Navigation
      • Surveillance
    • Systems Engineering Principles
    • Safety and Regulatory Context
    • Ethical considerations of unmanned and autonomous system
Intended learning outcomes On successful completion of this module you should be able to:
1. Contrast the main practical applications of AAM (including Unmanned traffic Management (UTM) and Urban Air Mobility [UAM]) and define their engineering subsystems.
2. Evaluate the main engineering challenges of AAM analysis and design.
3. Analyse qualitatively the functions and capabilities of the main subsystems of AAM
4. Debate the ethical concerns and regulatory challenges concerning unmanned and autonomous air traffic operations

Air Traffic Management Systems

Aim
    The aim of this module is to provide you with 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
    o Flight rules, airspace structures and classifications, air traffic service
    o ATC Technologies – communication, navigation and surveillance systems
    o ATC Procedures – airport separation, terminal and en route separation procedure
    o Safety, ethical and regulatory considerations
    • Future Air Navigation System
    o FANS Communication - required communication performance (RCP), AMSS, VDL, SSR Mode S
    o FANS Surveillance – required surveillance performance (RSP), ADS/ADS-B, SSR Mode S
    o FANS Navigation - Required Navigation Performance (RNP), Area Navigation (RNAV), GNSS and its augmentations, RNP/RNAV
    • Air Traffic Management
    o Capacity and delay models of airport and air routes
    o Air traffic flow management (ATFM) models
    o ATM automation decision support tools
Intended learning outcomes On successful completion of this module you should be able to:
1. Examine the current and future ATM ecosystem and its enabling infrastructure as proposed in SESAR/NextGen programmes.
2. Appraise the airspace classification and separation standards.
3. Critically evaluate the current ATC systems, functions of different ATC components and ATC procedures.
4. Formulate systems engineering approaches to the development of ATM components to meet future air traffic demands.
5. Analyse the performance of ATM systems, in a simulation environment using corresponding performance metrics
6. Evaluate the ethical and regulatory challenges when designing a new ATM system or service

Communications Systems

Aim
    This module aims at provide you with new skills and understanding of radio systems, air-to-air, air-to ground communications and overview of current approaches to line of sight and beyond line-of-sight techniques.

Syllabus
    • Overview of air-to-air and air-to-ground communication system
      • Voice/Audio communications
      • VHF air-to-ground communication
      • Satellite communications
      • L-band Digital Aeronautical Communications System
    • Airspace integration UAS/UAM/ ATM/ATC Technologies
      • BLOS and Satellite Data Link Connectivity
    • Communications system design
      • Review of models and techniques (e.g. Uplink/Downlink Model, Noise, SNR)
      • Link budget analysis
      • Antennas design & propagation
    • Communications Networking
      • Security considerations and techniques
      • Cyber-attack challenges and mitigations
Intended learning outcomes On successful completion of this module you should be able to:
1. Distinguish the fundamental principles of airborne/ground communication systems
2. Categorise different practices and procedures that is essential for air to ground communications
3. Estimate link budget analysis & communications system design
4. Assess different antenna design and propagation aspect for Line-of-sight/Beyond visual LOS (LOS/BLOS)
5. Evaluate security and networks techniques

Sensor Fusion

Aim
    The aim of this module is to provide an overview of sensor fusion architectures, algorithms and applications in the context of autonomous vehicles navigation, guidance and control both for linear and non-linear systems. The module aims also to you an understanding of the appropriate tools for error analysis, diagnostic statistics and heuristics enabling them to critically evaluate the performance of a sensor fusion architecture/algorithm. The main emphasis is on the Kalman Filter algorithm together with variants and generalisations, applied to target tracking problems.
Syllabus
    • Statistical Analysis (4 lectures)
    • Linear Kalman Filter and Linear Kalman Smoother (5 lectures)
    • Inertial navigation (3 lectures)
    • Constrained filters (1 lecture)
    • Sensor Integration architectures and Multiple sensor fusion (3 lectures)
    • Non-linear filters (EKF, UKF and Particle Filters) (5 lectures)
    • Case Study: Inertial navigation (3 lectures)
    • Case Study: Multiple sensor fusion (3 lectures)
Intended learning outcomes

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

1. Understand the fundamental principles in stochastic processes and in estimation theory.
2. Formulate, set up and execute the Kalman filter to linear processes and be able to assess the functional operation of the filter.
3. Formulate, set up and execute non-linear filters (Extended Kalman filter, Unscented Kalman Filter, Particle filters) to non-linear or non-Gaussian models.
4. List common motion models used in target tracking and navigation applications.
5. Design and appraise the performance of multi-sensor fusion architectures in a real-case scenario.

Intelligent Cyber Physical Systems

Aim
    The aim of this module is to enable you to think critically about technology, solutions, and gain best practices of intelligent systems issues relating to the cyber-physical systems.
Syllabus
    • Cyber-physical systems: Control, sensor and actuators
    • Intelligent agent and multi-agent 
    • Intelligent robotics  
    • Embedded systems 
    • Connected system 
    • Countermeasures. 
Intended learning outcomes

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

  • Appraise the theoretical and practical aspects for intelligent system in cyber-physical systems approach.
  • Distinguish the fundamental aspect of intelligent agent, robotics, multi agent systems. 
  • Create working knowledge in dependable control, and embedded systems. 
  • Assess key issues of connected system within the physical world.
  • Analyse different approaches of cyber-physical system with consideration of countermeasures.

Artificial Intelligence for Autonomous Systems

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 (2 lectures)
    • AI Algorithms: Unsupervised Learning (4 lectures)
    • Unsupervised Learning – Lab session (4 lectures)
    • AI algorithms: Supervised Learning – SVM and Neural Networks (5 lectures)
    • Supervised Learning – Lab session (3 lectures)
    • AI Algorithms: Supervised Learning – Deep Neural Networks (3 lectures)
    • Deep Learning – Lab session (3 lectures)
    • Automated Reasoning (2 lectures)
    • Case Study: AI for AS (2 lectures)
Intended learning outcomes

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

  1. Categorise AI methods for real-life scenarios of Autonomous Systems (AS) applications.
  2. Assess Applicability of Artificial Intelligence (AI) algorithms for AS.
  3. Set up the commonly used AI algorithms for application in the AS context.
  4. 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. Critically understand the fundamentals of the various guidance techniques and their properties.
2. Describe the algorithms that are required to produce an estimate of position and attitude;
3. Describe the characteristics, purposes, and design procedures of guidance and navigation systems;
4. Evaluate challenging problems in the guidance and navigation approaches for autonomous systems;
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.

 

Uncrewed Traffic Management

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 Module Intended Learning Outcomes On successful completion of this module a you should be able to:
1. Distinguish the emerging UTM and UAM enabling infrastructure, including navigation, surveillance sensors and automated systems.
2. Define and explain the regulatory and technical challenges of UTM and UAM (e.g. separation standards, conflict avoidance, automation).
3. Interpret the functional, technical, safety and regulatory targets for safe implementation of AAM applications.
4. Critically evaluate the different UTM and UAM ecosystems, their individual components and their related functions and services.
5. Analyse the performance of UTM and UAM systems, in a simulation environment using corresponding performance metrics.

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 Yan Xu.

Yan Xu

Dr Yan Xu

Lecturer in ATM/CNS
Antonios Tsourdos

Professor Antonios Tsourdos

Head of the Autonomous and Cyber-Physical Systems Centre
Gokhan Inalhan

Professor Gokhan Inalhan

BAE Systems Chair, Professor of Autonomous Systems and Artificial Intelligence
Saba Al-Rubaye

Dr Saba Al-Rubaye

Reader & DARTeC Fellow in Autonomous and Connected Systems
Ivan Petrunin

Dr Ivan Petrunin

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

Professor Hyo-sang Shin

Professor in Guidance, Navigation and Control
Tim Mackley

Dr Tim Mackley

Senior Lecturer

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

Industry-led education makes Cranfield graduates some of the most desirable all over the world for recruitment by both global primes 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, which include: From the 

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

Graduates from this course 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 as an autonomous systems engineer, design engineer, or in an operations role, at graduation, and in the future. Others decide to continue their education through PhD studies available within Cranfield University or elsewhere.

How to apply

Applications need to be made online. 

Once you have set up an account you will be able to create, save and amend your application form before submitting it.

Apply now