Master'sDefense, Military Engineering & Land Systems Applications open

Master’s in Resilient and GPS-Denied Autonomy

Online master's programme of 1,900 hours in resilient and GPS-denied autonomy. 12 modules covering inertial navigation, sensor fusion, visual odometry and SLAM, navigation by external references, robust perception, resilient planning, cooperation, cyber-resilience and V&V, with an assigned technical tutor.

Duration1,900 hours · 12–14 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma

Master’s in Resilient and GPS-Denied Autonomy — studying from Malta

Malta's defence sector is comparatively small, reflecting the country's neutrality and modest armed forces. Activity centres on the Armed Forces of Malta, maritime surveillance, border security and the associated technical support. Engineering roles are mostly found in equipment maintenance, communications, infrastructure and government procurement rather than large-scale defence manufacturing. Professionals may find opportunities through public service and contractors supplying the armed forces and related agencies, where reliability and compliance with national procurement rules are important.

Fees for Malta: €6,920 (Euro), with instalments and employer sponsorship available.

Relevant bodies in Malta

  • Armed Forces of Malta
  • Ministry for Home Affairs, National Security and Law Enforcement
  • Department of Contracts (Malta)
  • Malta Standards Authority (MSA)

Typical roles

  • Military Systems Engineer
  • Defence Equipment Maintenance Engineer
  • Procurement Engineer
  • Communications Systems Engineer

Questions from Malta

Which bodies regulate defence and military engineering work in Malta?

Malta's Armed Forces and the Ministry responsible for defence oversee military engineering and procurement. General engineering practice is regulated by the Chamber of Engineers (KSU) under the Engineering Profession Act. Export and dual-use controls follow EU and national rules. A SEIUM qualification is private and not accredited, so any recognition depends on employers or the relevant authority.

What career paths exist in Malta for land systems and defence engineering?

Opportunities in Malta are limited and mostly within government, the Armed Forces and their suppliers, with more substantial defence industry work typically found abroad. Many roles require security clearance and specific experience. A private qualification can support your profile, but you should expect employers to focus on experience and clearance eligibility.

How do I study this from Malta, and what should I consider about workload and funding?

The programme is delivered fully online in English, with an assigned technical tutor and a final project. You do not need a visa to study from home in Malta. Durations range from 300 to 1,900 hours, so plan time alongside any work commitments. Ask about instalment payments and whether employer sponsorship is available.

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Learning outcomes

What you will be able to do

01

Analyse the limitations of GNSS-dependent navigation.

02

Model and characterise inertial navigation systems.

03

Design robust multi-sensor fusion estimators.

04

Implement visual odometry, visual-inertial odometry and SLAM.

05

Apply navigation techniques based on external references.

06

Design autonomy that is resilient to sensor and communication degradation.

07

Validate a GNSS-free navigation system in the Master's Thesis.

Profile

Who it's for

  • Autonomous navigation engineers.
  • Sensor fusion engineers.
  • SLAM specialists.
  • Robotics engineers.
  • Autonomy verification engineers.
  • Researchers in resilient PNT.
  • Engineering or science graduates looking to enter the defence sector.
Standards & tools

What you'll work with

  • IEEE Std 952-2020 (single-axis interferometric fibre optic gyros)
  • IEEE Std 1293-2018 (linear single-axis accelerometers)
  • IEC 61508 (functional safety)
  • ISO 21448:2022 (SOTIF, safety of the intended functionality)
  • UL 4600 (safety of autonomous products)
  • RTCA DO-178C (airborne software)
  • US Executive Order 13905 (responsible use of PNT services)
  • ROS 2
  • GTSAM
  • ORB-SLAM3
  • Gazebo
  • MATLAB
Syllabus

12 modules

  1. 1.1GNSS dependence in autonomous systems
  2. 1.2Indoor, underground and urban canyon environments
  3. 1.3Interference and signal degradation
  4. 1.4Navigation requirements for autonomy
  5. 1.5Integrity and confidence of the solution
  6. 1.6Taxonomy of alternative techniques
  7. 1.7Resilient architectures
  8. 1.8Civil and defence use cases
  9. 1.9Performance metrics
  10. 1.10Case study: navigation requirements for a mission
  1. 2.1MEMS, FOG and RLG sensors
  2. 2.2Mechanisation equations
  3. 2.3Inertial errors and drift
  4. 2.4Initial alignment
  5. 2.5IMU calibration
  6. 2.6Allan variance
  7. 2.7Dead reckoning
  8. 2.8Pedestrian navigation with inertial sensors
  9. 2.9Selection of inertial grade
  10. 2.10Lab: IMU characterisation with Allan variance
  1. 3.1Kalman filter
  2. 3.2EKF and UKF filters
  3. 3.3Particle filters
  4. 3.4Factor graph optimisation
  5. 3.5Loose and tight coupling
  6. 3.6System observability
  7. 3.7Fault detection and exclusion
  8. 3.8Estimator consistency
  9. 3.9The GTSAM library
  10. 3.10Exercise: inertial fusion with wheel odometry
  1. 4.1Camera geometry
  2. 4.2Monocular and stereo visual odometry
  3. 4.3ORB-SLAM3
  4. 4.4Visual-inertial odometry
  5. 4.5LiDAR SLAM
  6. 4.6Loop closure
  7. 4.7Dense and sparse maps
  8. 4.8Failures in low-texture environments
  9. 4.9Evaluation with KITTI and EuRoC
  10. 4.10Lab: visual-inertial odometry with EuRoC
  1. 5.1Terrain correlation
  2. 5.2Magnetic anomaly navigation
  3. 5.3Gravimetric navigation
  4. 5.4Celestial navigation
  5. 5.5Image-to-map correlation
  6. 5.6Signals of opportunity
  7. 5.7eLoran terrestrial radionavigation
  8. 5.8Bathymetric underwater navigation
  9. 5.9Geospatial databases
  10. 5.10Case study: terrain correlation navigation
  1. 6.1Sensors in adverse conditions
  2. 6.2Millimetre-wave radar
  3. 6.3Thermal cameras
  4. 6.4Event cameras
  5. 6.5Multimodal fusion
  6. 6.6Robustness against adversarial perturbations
  7. 6.7Sensor anomaly detection
  8. 6.8Deep learning for perception
  9. 6.9Uncertainty in neural networks
  10. 6.10Workshop: radar-camera fusion
  1. 7.1Planning under uncertainty
  2. 7.2POMDP models
  3. 7.3Localisation-aware planning
  4. 7.4Real-time replanning
  5. 7.5Behaviour trees
  6. 7.6Management of degraded modes
  7. 7.7Adaptive autonomy
  8. 7.8Decision-making with limited communications
  9. 7.9Behaviour validation
  10. 7.10Exercise: planner with position uncertainty
  1. 8.1Cooperative localisation
  2. 8.2UWB ranging
  3. 8.3Distributed consensus
  4. 8.4Ad hoc networks
  5. 8.5Intermittent communications
  6. 8.6Shared maps
  7. 8.7Time synchronisation without GNSS
  8. 8.8Security in cooperation
  9. 8.9Fleet scalability
  10. 8.10Lab: cooperative localisation with UWB
  1. 9.1Threats to sensors
  2. 9.2Detection of spoofed signals
  3. 9.3Authentication of navigation data
  4. 9.4Secure autonomy architecture
  5. 9.5Fault tolerance
  6. 9.6Runtime monitoring
  7. 9.7Recovery after incidents
  8. 9.8Risk analysis
  9. 9.9Ethics of autonomy in degraded environments
  10. 9.10Case study: threat analysis of navigation
  1. 10.1Simulation with Gazebo and AirSim
  2. 10.2Hardware-in-the-loop
  3. 10.3Field tests without GNSS coverage
  4. 10.4ATE and RPE metrics
  5. 10.5Corner-case scenarios
  6. 10.6Certification of autonomous functions
  7. 10.7Robustness testing
  8. 10.8Test data management
  9. 10.9Applied functional safety
  10. 10.10Exercise: evaluation campaign for a VIO system
  1. 11.1Indoor drones
  2. 11.2Vehicles in mines and tunnels
  3. 11.3Underwater vehicles
  4. 11.4Planetary exploration
  5. 11.5Rescue and emergencies
  6. 11.6Agriculture and logistics
  7. 11.7Defence platforms
  8. 11.8Lessons from the DARPA SubT Challenge
  9. 11.9Regulations by domain
  10. 11.10Case study: architecture for underground inspection
  1. 12.1Statement of the navigation problem
  2. 12.2State-of-the-art review
  3. 12.3Sensor selection
  4. 12.4Estimation architecture
  5. 12.5Implementation in ROS 2
  6. 12.6Validation in simulation
  7. 12.7Experimental testing
  8. 12.8Conclusions and future work
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on SEIUM's campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so it fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is ROS 2, GTSAM, ORB-SLAM3, Gazebo and MATLAB, in free, academic or evaluation versions. Testing is studied through real procedures: characterisation of inertial units, evaluation of visual-inertial odometry in GNSS-free environments and cooperative localisation tests with UWB. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: IEEE Std 952-2020, IEEE Std 1293-2018, IEC 61508, ISO 21448:2022, UL 4600, RTCA DO-178C and US Executive Order 13905.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of IEEE Std 952-2020, IEEE Std 1293-2018 and IEC 61508 applied to real cases, not as a theoretical annex.
Industry software:ROS 2, GTSAM, ORB-SLAM3, Gazebo and MATLAB, in free, academic or evaluation versions.
Master's Thesis with defence:a GNSS-free navigation system implemented in ROS 2 and validated in simulation and experimental tests, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

IMU characterisation
Characterise an inertial unit using Allan variance.
Visual-inertial odometry
Implement and evaluate VIO on the EuRoC dataset.
Cooperative localisation
Localise a team of platforms with UWB ranging.
VIO evaluation campaign
Assess a VIO system with ATE and RPE metrics.
Master's Thesis
A GNSS-free navigation system in ROS 2, defended before a panel.
Career opportunities

Where this takes you

Autonomous navigation engineerSensor fusion engineerSLAM specialistRobotics engineerAutonomy verification engineerResilient PNT researcher
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer able to run the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with ROS 2, GTSAM, ORB-SLAM3, Gazebo and MATLAB, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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