SWAGºÏ¼¯

• Professor Antonios Tsourdos

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.

• 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 SWAGºÏ¼¯ Virtual Learning Environment.

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.

• Professor Antonios Tsourdos

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.

• 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.

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.

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.

• 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.

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.

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.

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.

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.

• Professor Saba Al-Rubaye

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.

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.

 

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.

• Professor Antonios Tsourdos

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.

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.

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.

• Dr Ivan Petrunin

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.

• 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.

 

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.

• Dr Ivan Petrunin

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

• 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 SWAGºÏ¼¯: AI for AS.

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.

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.

• 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.

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,