📖Program Curriculum
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:
Categorise 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. 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
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